No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Fast method to predict building heating demand based on the design of experiments
Abstract
A rational choice of solutions for low energy building requires a performance evaluation for a large number of parameter combinations. Currently, building thermal dynamic simulation is used to check design solutions but it gives little information on what and how much may be improved. Iterative parameter optimization is a solution but the methods used may stick in local minima and do not allow the designer to evaluate different solutions. An alternative evaluation method is to obtain simple polynomial functions which estimate the annual energy demand as a function of building envelope parameters. The coefficients of these functions may be obtained by regression from dynamic simulation results. The number of dynamic simulations needed is reduced if the numerical simulations are optimally designed. The polynomial functions may be used to predict alternative performances from which the solution may be chosen. The designer is guided in his choice by the coefficients of the polynomial function which give the effect of each parameter on energy demand.
... Використання геометричних характеристик будівель як орієнтовно-го показника їхнього потенційного енергоспоживання розглядалося раніше в багатьох дослідженнях. У ряді публікацій наголошується на використанні CityGML стандартів для отримання топологічно цілісних моделей будівель [11,14]. Залежно від рівня деталізації, такі моделі можуть містити інформацію про площу, висоту, об'єм, не тільки цілих будівель, а й окремих кімнат будівель, що суттєво уточнює потенційні розрахунки енергоспоживання. ...
... Залежно від рівня деталізації, такі моделі можуть містити інформацію про площу, висоту, об'єм, не тільки цілих будівель, а й окремих кімнат будівель, що суттєво уточнює потенційні розрахунки енергоспоживання. У розрахунках ЕС будівель можуть використовуватися досить складні моделі, що передбачають, як, наприклад, втрати тепла як через зовнішню оболонку будівлі [25,27], так і такі, які навіть враховують загальний енергетичний баланс будівлі [14]. У підходах, що описуються, важливим аспектом було визначення сегментів моделі будівлі, що є відповідно внутрішніми / зовнішніми стінами, дахом і підлогою. ...
... Так, середньорічне ЕС за класом житлової забудови за інших рівних факторів може бути на 15-30 % вище, ніж за класом комерційних (нежитлових) будівель [8,13,20,26]. Що ж до віку будівлі, то тут зовсім простежується чітка і цілком зрозуміла закономірність підвищення середньорічного ЕС будівлі разом із підвищенням віку будівлі [8,14]. ...
У статті подається підхід до застосування просторової оцінки енергоспоживання (ЕС) міських забудов та урбогеосистемного аналізу отриманих результатів. Оцінка ЕС будівель передбачає встановлення кореляційної залежності між їх енерговитратами та відповідними геометричними характеристиками, зокрема висотою та об’ємом забудови. Для автоматизованого виокремлення з високою точністю цих характеристик будівель авторами пропонується використання даних дистанційного лазерного сканування (лідарних даних). Наведено оригінальний підхід до обробки та аналізу лідарних даних інструментами авторського веб-ГІС додатку з метою виокремлення та моделювання будівель. Побудовані моделі будівель зберігають у якості атрибутів їх точні геометричні характеристики та узагальнені архітектурні властивості. Подається методика розрахунку ЕС будівель, в якій використовується їх геометрична інформація, та інформація щодо їх віку й типу, які також є кореляційно залежними з енерговитратами будівель. За отриманою з лідарних даних геометрією будівель визначається показник їх корисної площі (призначеної для опалення). Для оцінки ЕС приймаються дані щодо енергоспоживання будівель з реальних показників лічильників, які екстраполюються на розрахований показник корисної площі будівель. Побудовано семантичну таблицю, яка корегує розрахований показник ЕС будівлі, в залежності від її віку й типу, та визначає остаточний клас енергоефективності будівлі. За наведеними методиками побудовано та візуалізовано тривимірні моделі забудов для міст Амстердам та Ейндховен, із застосованою кольоровою гамою до будівель, що відображає класи їх енергоефективності. Розкрито сутність урбогеосистемного анадізу міського середовища у контексті дослідження міського ЕС. На підставі отриманої візуалізації просторового розподілу міського ЕС виокремлено певні закономірності такого розподілу між окремими міськими забудовами та визначено фактори, що впливають на рівень даного показника.
... These issues can be addressed, from a computational perspective, by means of a parametric and probabilistic analysis, used as an exploratory tool and for optimization purposes (Tronchin et al., 2016;Østergård et al., 2020). The importance of accounting for multiple performance scenarios (Shiel et al., 2018), considering the impact of both technical and human factors (Yoshino et al., 2017), is becoming evident, and the Design Of Experiments technique is being used in many cases for building energy performance simulations (Jaffal et al., 2009;Kotireddy et al., 2018;Schlueter and Geyer, 2018;Tronchin et al., 2018a). With respect to human factors influencing performance, generally occupants' comfort preferences and behaviours (Menezes et al., 2012;Tagliabue et al., 2016;Gaetani et al., 2018) are overlooked, even though they can create a relevant gap between simulated and measured performance. ...
... In this section, we illustrate some examples of regression models used in design phase's performance analysis, which makes use of datasets that partially overlap with the ones reported in Table 1. First of all, there are many examples of applications of regression models for early stage design evaluation (Catalina et al., 2008;Hygh et al., 2012;Asadi et al., 2014;Al Gharably et al., 2016;Ipbüker et al., 2016), also using a Design of Experiments approach (Jaffal et al., 2009) to create multiple input combinations in a rational way. Further, we can find examples of application of energy signatures analysis from design to operation (Allard et al., 2018;Tronchin et al., 2018a), leading to a continuity in the data analysis workflow. ...
... Design phase assessment Early design assessment (Catalina et al., 2008;Hygh et al., 2012;Asadi et al., 2014;Al Gharably et al., 2016;Ipbüker et al., 2016) Design of Experiments and parametric design (Jaffal et al., 2009) Energy signatures from design to operation (Allard et al., 2018;Tronchin et al., 2018a) Techno-economic analysis Design optimization considering life cycle cost (LCC) a simplified energy modelling (Aparicio-Ruiz et al., 2019) Data-driven cost optimization, using data envelopment analysis (DEA) (Kavousian and Rajagopal, 2013) Modelling for Energy Performance Contracting (EPC) from design to operation (Ligier et al., 2017) Frontiers in Energy Research | www.frontiersin.org November 2020 | Volume 8 | Article 557649 5 ...
The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase's energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.
... Catalina et al. [14] built a multiple regression model that was based on the building global heat loss coefficient, the south-facing equivalent surface, and the temperature difference to determine the heating demand. Jaffal et al. [15] utilized an alternative evaluation regression model to model the UK annual heat demand according to dynamic simulation results. Regression models have also been used for electricity modelling. ...
... The difference between indoor and outdoor temperatures is the most important factor affecting heat demand [12][13][14][15][16][17][18][19][20]. Under the premise that the indoor temperature is set at a fixed value, the outdoor temperature is the determining factor for the heat load. ...
... KNN 1, 5,6,7,8,9,11,12,13,14,16,18,19 2,3,4,7,9,11,12,13,14,18,19 SVR 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19 2,3,4,11,12,13,14,17 NB 1,6,7,9,10,11,12,13,14,16,18,19 2,3,5,6,7,9,11,12,13,14 LMSR (feature selected by embedded F-test) 1, 3,4,6,8,9,10,11,13,14,17 2,3,5,6,9,11,14,15,16,17,18 From the selection results, it is clear that the lagged variable is vital in predicting the same energy type, while it has only limited predicting power for the other type. Furthermore, the selection succeeded in suggesting that temperature and historical records are the main drivers of heating demand, while some user behavior in the form of 'agent schedule' can improve the model's accuracy. ...
There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of 'agent schedules' that provide proxy information that is based on broad classes of activity undertaken by the building's inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the 'agent schedules', which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all 'agent schedule' features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.
... Ces étapes seront détaillées davantage dans la section suivante. D'autre part, la méthode des plans d'expériences a été adoptée dans le domaine de l'efficacité énergétique des bâtiments par plusieurs auteurs (Flory-Celini 2008, Jaffal et al. 2009, Chlela et al. 2009, Romani et al. 2015. ...
... Les auteurs ont considéré un appartement de 100m² avec plusieurs éléments: épaisseurs d'isolation d'enveloppe, type de vitrage, coefficients d'absorption des murs et du toit, taux de renouvellement d'air et stores de fenêtres. Les modèles polynomiaux considérés pour les résultats des simulations sont les mêmes que ceux utilisés par Jaffal et al. (2009). Les auteurs ont confirmé que la charge de refroidissement prédite par les modèles quadratiques et d'interaction complets montre un bon accord avec les résultats de la simulation. ...
... Basé sur un calcul mathématique à l'aide du code « Design Expert », le DOE permet d'obtenir les informations nécessaires limitant ainsi le nombre d'expériences à un niveau réaliste. De nombreux auteurs ont utilisé cette méthode pour l'optimisation des simulations de demande d'énergie du bâtiment (Romani et al. 2015, Flory-Celini 2008, Jaffal et al. 2009, Chlela et al. 2009 ...
La conception de constructions bioclimatiques prend une place capitale dans les préoccupations des spécialistes du bâtiment dans le monde entier et la consommation d’énergie pour le conditionnement des intérieurs est d’autant plus élevée que les dégâts écologiques y afférents deviennent alarmants. L’objectif de ce travail s’inscrit dans cette optique en mettant à disposition un guide de conception de bâtiment à consommation d’énergie réduite en matière de chauffage et de refroidissement. Concrètement, Cette thèse vise à étudier l'impact de nombreux paramètres techniques, architecturaux et météorologiques d'un appartement sur sa performance énergétique et son confort thermique. Les paramètres étudiés sont: l'isolation thermique de l'enveloppe, l'orientation, le niveau de l’étage, le couplage du plancher bas au sol, le coefficient d'absorption du toit et des murs extérieurs et la ventilation mécanique contrôlée. Une étude numérique basée sur TRNSYS est réalisée dans les six climats différents du Maroc tels qu’ils sont définis par le Règlement Thermique des Constructions au Maroc à savoir ; Atlantique : Agadir, Méditerranéen : Tanger, Continental : Fès, Froid : Ifrane, Semi-aride : Marrakech et Désertique : Errachidia. Le modèle numérique a été validé par rapport aux résultats expérimentaux obtenus lors de longues campagnes de suivi expérimental en été et en hiver d’un appartement situé dans la ville de Marrakech. Les charges de chauffage et de refroidissement de l'appartement ainsi que les indices d'inconfort thermique sont calculés pour les onze configurations possibles combinant les variantes des paramètres étudiés. Les résultats montrent que l'isolation thermique de la façade conduit à une surchauffe estivale avec une augmentation de la charge thermique totale de l’appartement pouvant atteindre 18% dans tous les climats considérés, à l'exception du climat froid. Il est constaté que la technique d’isolation thermique de la façade par la lame d’air, en empêchant la surchauffe estivale, est suffisante pour atteindre un niveau appréciable d’efficacité énergétique et de confort thermique. De même, il est établi que l’isolation thermique du plancher bas sur sol provoque une augmentation de la charge thermique d’au moins 67% pour les climats chauds et modérés. La meilleure combinaison de toutes les mesures d'efficacité énergétique étudiées pour chaque condition climatique est évaluée par la comparaison des charges thermiques avec celles d’un cas de référence qui représente l'appartement réel. Préalablement à cette approche expérimentale, une étude théorique basée sur la méthode des plans d’expérience et sur la régression linéaire est mise en œuvre afin de prédire, à travers des modèles mathématiques, les charges de refroidissement et de chauffage d’une cellule de bâtiment à Marrakech au climat chaud et semi-aride ainsi que d’hiérarchiser les facteurs à haut impact sur les performances énergétiques du bâtiment dans cette région.L'analyse des modèles mathématiques élaborés sur la base des résultats de plusieurs simulations dynamiques à l’aide de TRNSYS, compte tenu de la haute précision des coefficients de corrélation obtenus (supérieurs à 0.92), permet de conclure que dans le climat de Marrakech, les paramètres du bâtiment qui ont un impact important sur sa charge thermique sont principalement le taux de baies vitrées, l'isolation thermique du toit, des murs et du plancher bas. Enfin, cette approche peut être considérée, d’une part, comme un carrefour entre l’énergétique des bâtiments, l’architecture ainsi que les méthodes mathématiques (plans d'expériences et régression multilinéaire) qui apporte une valeur ajoutée du point de vue méthodologique et d’autre part comme un outil d’évaluation préalable à la construction des bâtiments sous les différents climats étudiés.
... A building's heating and cooling load, and hence its energy consumption, can be reduced by designing or renovating the building in a way that minimizes these loads. In many cases, a dynamic building energy simulation is used to estimate building cooling and heating loads since no actual building exists during the building design phase [3][4][5][6][7][8][9][10][11][12]. By analyzing the simulation results, it is possible to evaluate the impact and sensitivity of the design factors on the loads. ...
... OFAT is an excellent method for understanding the functional relationship between the design factors and responses if there is no interaction between the design factors. However, it has been reported that interactions of the building envelope design factors affect the loads significantly [5,9,19]. Factorial design is an experimental design that considers the interactions between the design factors. ...
Indoor solar-heating systems that use ventilated roofs have drawn attention in recent years. The effectiveness and efficiency of such air-heating systems vary depending on the design and operation methods. In Japan, by introducing outside air into a ventilated roof cavity and circulating the air indoors, systems that simultaneously obtain ventilation, solar heating, and heat-storage effects have been actively developed. The conventional systems intake a large volume of outside air to increase the solar heat collection effect. However, there is a risk of heat loss and over-drying when a large amount of cold dry air during winter is introduced. In this paper, plans are presented for improving these solar heating and heat-storage effects by preventing over-drying using indoor air circulation via ventilated cavities in the roof and indoor wall. By comparing the results of the proposed system with those of the conventional system via numerical simulation, the heating load is found to be reduced by 50% or more by circulating indoor air to the ventilated roof and storing the heat in the indoor wall. Moreover, an increased relative humidity of approximately 10% was confirmed by reducing the intrusion of the outside air and keeping the moisture indoors.
... A building's heating and cooling load, and hence its energy consumption, can be reduced by designing or renovating the building in a way that minimizes these loads. In many cases, a dynamic building energy simulation is used to estimate building cooling and heating loads since no actual building exists during the building design phase [3][4][5][6][7][8][9][10][11][12]. By analyzing the simulation results, it is possible to evaluate the impact and sensitivity of the design factors on the loads. ...
... OFAT is an excellent method for understanding the functional relationship between the design factors and responses if there is no interaction between the design factors. However, it has been reported that interactions of the building envelope design factors affect the loads significantly [5,9,19]. Factorial design is an experimental design that considers the interactions between the design factors. ...
Interest in research analyzing and predicting energy loads and consumption in the early stages of building design using meta-models has constantly increased in recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this study, Latin Hypercube Sampling (LHS) is proposed as an alternative to Fractional Factor Design (FFD), since it can improve the accuracy while including the nonlinear effect of design parameters with a smaller size of data. Building energy loads of an office floor with ten design parameters were selected as the meta-models' objectives, and were developed using the two sampling methods. The accuracy of predicting the heating/cooling loads of the meta-models for alternative floor designs was compared. For the considered ranges of design parameters, window insulation (WDI) and Solar Heat Gain Coefficient (SHGC) were found to have nonlinear characteristics on cooling and heating loads. LHS showed better prediction accuracy compared to FFD, since LHS considers the nonlinear impacts for a given number of treatments. It is always a good idea to use LHS over FFD for a given number of treatments, since the existence of nonlinearity in the relation is not pre-existing information.
... For those EGP locations, where the UCE was implemented (Boston, Munster, and the city of Lyon, France), its workflow was accomplished in the similar way: 1. Sets of .OBJ files and .JSON files corresponding to 3D CityGML LOD1 models have been obtained for a given location by opensource Lidar data (both .LAZ, and .LAS formats) processing with ELiT software. 2. On the base of the UCP information sources, from which the urban land-use classes have been obtained, and according to the empirical content of some existing references besides those already mentioned above [100,101], we have made the following assumption. Upon a condition of all other equal factors nonresidential (commercial) buildings may consume the energy up to 15-30% fewer, that residential ones. ...
... Another key attribute is constructionYear, according to an accepted regularity: the older a building, the more energy it consumes [98,100]. 4. One more attribute-storeysAboveGround is either obtained from the OSM-source, or calculated as the similar parameter, which is in the UCP. 5. If there is the information (from OSM or form some municipal sources) assignable for some separate buildings, which concerns values kWh/m 2 a and kWh/m 2 year, it should be used for comparison of actual values versus calculated ones. ...
Our research presents a complete R&D cycle-from the urban terrain generation and feature extraction by raw LiDAR data processing, through visualizing a huge number of urban features, and till applied thematic use cases based on these features extracted and modeled. Firstly, the paper focuses on the original contribution to algorithmic solutions concerning the fully automated extraction of building models with the urban terrain generation. Topography modeling and extraction of buildings, as two key constituents of the robust algorithmic pipeline, have been examined. The architectural scheme of the multifunctional software family-EOS LIDAR Tool (ELiT) has been presented with characteristics of its key functionalities and examples of a user interface. Both desktop, and web server software, as well as a cloud-based application, ELiT Geoportal (EGP), as an entity for online geospatial services, have been elaborated on the base of the approach presented. Further emphasis on the web-visualization with Cesium 3D Tiles has demonstrated the original algorithm for efficient feature visualizing though the EGP locations. Summarizing presentation of two thematic use-cases has finalized this research, demonstrating those applied tasks, which can be efficiently resolved with the workflow presented. A necessity of a conclusive workflow elaboration for use cases, which would be based on the actual semantics, has been emphasized.
... Thus, a second limitation lies in the lack of access to certain technical data that these programs require as input values due to the unavailability of project documentation on municipal buildings, whether because of their age, poor information management, resource scarcity, etc. Although simplified simulation tools are being developed with different approaches [27,28], the use of a computer tool does not relieve users from the need to be acquainted with the know-how and management of a wide range of rules, regulations, and technical instructions involved in decision-making. ...
... Although simplified calculation methods are being developed with different approaches [27,28], our aim is to develop a method to quantify the thermal energy requirements, specifically heating and DHW, with an ability to contemplate the complexity involving the calculations and simplicity in its implementation. The primary energy, translated into monetary values and depending on the fuel used, will allow us to estimate the annual cost local governments are facing in the current conditions and how this would change with renewable energies [29]. ...
Climate change, other environmental impacts due to increased energy use worldwide, and the exhaustion of energy resources are some of the major challenges facing today’s society. Considering this, this paper assesses the importance of biomass-based heating and hot-water systems in the achievement of more sustainable buildings. Using a simplified calculation method, we jointly analyzed the potential operational cost savings and reduction of CO2 emissions that would be achieved when the traditional energy model, based on the use of fossil fuels, is replaced by biomass-based heating systems. Evidence stems from a case study in public buildings in the province of Pontevedra, in the northwest of Spain. The results of this research not only show a huge impact on CO2 emission reduction just by adapting the kind of fuel use, but also considerable annual cost reduction without compromising activity development and workers’ comfort. Thus, the findings obtained should encourage governments to support the transition toward cleaner sources of energy, acting as first movers toward a locally produced and renewable-based energy supply.
... The above research promotes the observation that the complex energy demand models considered, i.e. dynamic simulation tools or non-linear models, result in a highly complex problem for a single-building retrofit optimization. The complexity and temporal resolution of the underlying models induces prohibitively high data and time requirements when the problem is scaled up [18]. Moreover, the impacts of the urban environment on the behavior of buildings must be considered [19]. ...
... Steady-state models are widely observed in the academic literature for energy demand modeling, at both individual-scale [18,24,25] and urban-scale [30][31][32]. They are also employed by authorities for establishing Energy Performance Certificates [33]. ...
Reduction of energy consumption in the building sector has been identified as a major instrument to tackle global climate change and improve sustainability. In this paper, we propose a methodology to address a retrofit planning problem at a community level, with a building resolution. The resulting tool helps local decision-makers identify pertinent actions to improve the environmental behavior of their territories. Two building retrofit levers are considered, namely envelope insulation and heating systems replacement. Retrofit planning is treated here as a single-objective optimization problem aimed at reducing the total costs of retrofit actions by minimizing their net present value. A multidimensional multiple-choice knapsack problem formulation is proposed through the adoption of adequate decision variables. It suitably balances the complexity induced by the large number of potential retrofit action combinations and the number of variables in the problem and permits a linear formulation. An optimization of virtual building stocks is performed to highlight the developed model’s capacity to tackle large problems (5,000 buildings) in a few minutes. Finally, three analyses finally are led on a real case-study territory, featuring both appropriate retrofit solutions and building stock information. Long-term evaluation of retrofit strategies over the short-term results in an additional 10% reduction of energy consumption and greenhouse gases emissions and encourages thermal insulation. When targeting a 40% reduction in energy demand, retrofit costs ranging from 20 to 800€/m² are observed. Finally, the developed method was used to draw a CO² abatement cost curve at territory level. A 70% reduction of emissions can be achieved with costs under 50 €/tCO2e.
... As a result, it is often difficult to evaluate the reliability and authenticity of the results. New "hourglass" approaches [33,34] have begun to address some of the shortcomings of archetype-based models as they combine reductive archetype models with a re-diversification process in order to add stochastic variations to individual buildings and re-introduce diversity lost in the reductive archetype process. Moreover, reduced-order methods have also been developed to model urban energy use, including electrical circuit analogy based on resister-capacitor networks [35], energy demand calculations based on quasi-static monthly energy balance [36], degree-day estimations based on heat transfer coefficient [33], steady-state methods based on energy balance equations [19], thermal shoebox models based on insolation analysis and clustering [31] and reducedcomplexity models based on simplified state space methods [37]. ...
... New "hourglass" approaches [33,34] have begun to address some of the shortcomings of archetype-based models as they combine reductive archetype models with a re-diversification process in order to add stochastic variations to individual buildings and re-introduce diversity lost in the reductive archetype process. Moreover, reduced-order methods have also been developed to model urban energy use, including electrical circuit analogy based on resister-capacitor networks [35], energy demand calculations based on quasi-static monthly energy balance [36], degree-day estimations based on heat transfer coefficient [33], steady-state methods based on energy balance equations [19], thermal shoebox models based on insolation analysis and clustering [31] and reducedcomplexity models based on simplified state space methods [37]. However, such reduced-order methods often require large oversimplifications (e.g., a building is modeled as single thermal zone) [31] and require making several strong assumptions (e.g., the heating set point is constant) [19]. ...
... Its reliability has been ensured by its painstaking construction, which was informed by previous research, and by the extensive statistical analysis that was done to understand its features and distribution. The inclusion of these eight key input variables reinforces the dataset's robustness and establishes a strong foundation for the development of predictive models in the realm of building heating requirements (Jaffal et al. 2009). ...
The importance of energy-efficient building management strategies has grown in study and practice today. To address the urgency, this study integrates exact heating demand projections with powerful optimization algorithms to provide a complete solution. This research explores the complex task of energy optimization in HVAC systems, requiring careful analysis and creative problem-solving. This study highlights the importance of accurate heating load forecasting in improving HVAC system efficiency, energy conservation, and cost efficiency. The SVR model is fused with 2 complex optimization algorithms, the Coronavirus Herd Immunity Optimizer (CHIO) and the Honey Badger Algorithm (HBA), in a groundbreaking methodology. The main goals are to improve heating load calculations and streamline HVAC system optimization. This study validates the importance of accurate heating load forecasting for cost-effectiveness, energy efficiency, and environmental sustainability in building operations. The SVHB model outperforms other models with a low RMSE value of 0.860 (kW) and a maximum R² value of 0.993, indicating higher predictive accuracy and explanation. To meet the growing demand for energy-efficient building management, this research combines advanced algorithms with accurate heating load estimates for HVAC systems. These findings highlight the importance of precise heating demand forecasts for both cost-effective building operations and environmental responsibility.
... Thiangchanta and Chaichana [13] developed a multiple linear regression-based simplified model that predicts the actual cooling and heating loads of air-conditioned rooms in Thailand with an impressive accuracy, showing only a 0.15% error margin. Similarly, Jaffal et al. [14] proposed a straightforward regression model utilizing 11 selected parameters for the quick estimation of heating demand in single-family homes across French continental, maritime, and Mediterranean climates. In another study, Catalina et al. [11] established a multiple regression model based on five principal factors affecting building heating energy consumption, enabling accurate predictions of monthly heating demand for residential buildings in France. ...
... Thiangchanta and Chaichana [13] developed a multiple linear regression-based simplified model that predicts the actual cooling and heating loads of air-conditioned rooms in Thailand with an impressive accuracy, showing only a 0.15% error margin. Similarly, Jaffal et al. [14] proposed a straightforward regression model utilizing 11 selected parameters for the quick estimation of heating demand in single-family homes across French continental, maritime, and Mediterranean climates. In another study, Catalina et al. [11] established a multiple regression model based on five principal factors affecting building heating energy consumption, enabling accurate predictions of monthly heating demand for residential buildings in France. ...
... section 3.1 for details), and the number of runs (4×3 14 ) required for the full factorial design of experiments was beyond the ability to conduct the experiments and to analyze the results; thus, the fractional factorial experiments was applied to replace the former method. A massive number of simulations increases the precision of the models [9]. 40,000 random combinations, which is only 0.2% of the number of runs required for the full factorial experiment, were used to perform the simulation of peak cooling load. ...
This study uses the Monte Carlo method and building performance simulations to develop an additive model for rapid peak load forecasting at design phase that considers the effects of design parameters. The Monte Carlo method generates numerous of simulation cases and EnergyPlus software is used for the calculations. Specifically, a total of 20 parameters were considered for analysing the peak load calculations, including design day conditions, envelope performance, infiltration, etc. An office building was selected as the reference building. With the screening experiments and the standard regression coefficient, it was identified that there are 15 important parameters for peak cooling load in the perimeter zones and 7 in the core zone. Main effects and interactions for selected parameters were determined by factorial experiments of 40,000 runs for the perimeter zone and 1,287 runs for the core zone. Main effects and interactions were used to develop an additive model between design parameters and peak cooling loads. Finally, model validation by additional 1,000 cases shows a coefficient of determination of 0.995, with a mean bias error of 3.2%, and a coefficient of variation of 3.7%, which indicated that the developed additive model had high accuracy.
... Also, Yao and Zhu compared thermotropic double-glazed windows, double-glazed windows, and tinted double-glazed windows [40,41]. In this case, some simple experience models were also built in this area to quickly assess energy consumption [42][43][44][45]. ...
The concepts of visual comfort and lighting quality are among key components of architectural design. Although the building industry shows significant signs of progress in this regard, frameworks aiming at the integration of comfort conditions remains one of the main points of contention. In recent years many studies have been carried out about the potential of window sizes to reduce energy consumption. However, the existing body of literature is lacking regarding various variables on visual comfort and consequently energy efficiency at the same time. Therefore, this research proposes a model to fill this gap in architectural regulations. Building on a genetic algorithm, the study develops and examines a novel multi-objective optimization model which can be adapted in the building design stage. The examined model leads to develop a standard strategy that can be used to evaluate visual comfort while maximizing energy performance in buildings with diverse functions and located in various climates. This model is formed from three main evaluation steps, which can be named project modeling according to the design stage, comfort assessment and evaluation, optimization and decision-making process. Therefore, based on the research limitation, window-to-wall ratio and types of glazing materials have been selected as optimization variables to examine the methodology. In this regard, the results show that the range between 20 and 50% can be considered acceptable in the examined office building located in Northern Cyprus. Although 20–40% clear glass makes an acceptable range of opening, the most preferred range is 30–50% of reflective glass, which proposes a bigger opening with the same amount of energy performance.
... Also, Yao and Zhu compared thermotropic double-glazed windows, double-glazed windows, and tinted double-glazed windows [40,41]. In this case, some simple experience models were also built in this area to quickly assess energy consumption [42][43][44][45]. ...
The concepts of visual comfort and lighting quality are among key components of architectural design. Although the building industry shows significant signs of progress in this regard, frameworks aiming at the integration of comfort conditions remains one of the main points of contention. In recent years many studies have been carried out about the potential of window sizes to reduce energy consumption. However, the existing body of literature is lacking regarding various variables on visual comfort and consequently energy efficiency at the same time. Therefore, this research proposes a model to fill this gap in architectural regulations. Building on a genetic algorithm, the study develops and examines a novel multi-objective optimization model which can be adapted in the building design stage. The examined model leads to develop a standard strategy that can be used to evaluate visual comfort while maximizing energy performance in buildings with diverse functions and located in various climates. This model is formed from three main evaluation steps, which can be named project modeling according to the design stage, comfort assessment and evaluation, optimization and decision-making process. Therefore, based on the research limitation, window-to-wall ratio and types of glazing materials have been selected as optimization variables to examine the methodology. In this regard, the results show that the range between 20 and 50% can be considered acceptable in the examined office building located in Northern Cyprus. Although 20–40% clear glass makes an acceptable range of opening, the most preferred range is 30–50% of reflective glass, which proposes a bigger opening with the same amount of energy performance.
... In building energy forecasting, the inputs sample is usually built using a representative method [17]. Various experimental design procedures, such as one factor at a time, factorial factor design, or Latin hypercube sampling (LHS), are categorized based on how the design conditions are prepared [24,25]. LHS is one of the most popular techniques for creating a constrained set of variables with a limited and representative sample of the population. ...
In this paper, a metamodel-based approach involving simulation data collection and data-driven techniques was used to forecast and optimize heating and cooling loads in three different climates in Morocco. The metamodel method is gaining popularity as it offers a better balance between accuracy and calculation time. In addition, a wrapper method was used as a feature selection approach to find the best feature subsets in order to reduce models’ complexity. Therefore, the performances of eleven state-of-the-art algorithms including evolutionary-based algorithms, swarm intelligence-based algorithms and human-based algorithms, coupled with a learner such as Artificial Neural Networks (ANNs) and Support vector machine (SVM), were assessed. Hybrid model based on League championship algorithm (LCA), Discrete symbiotic organisms search (DSOS) algorithm, Particle swarm optimization (PSO) algorithm showed better results in terms of accuracy and reduction of feature input parameters. Indeed, the best desired performances were obtained with LCA-SVM for cooling load, DSOS-SVM for heating load, PSO-ANN for both heating and cooling loads. On the other hand, to optimize the annual thermal load, the NSGA-II algorithm was used. Results showed a reduction of 68% of the total annual load compared to the base case in Meknes city, 73% in Ifrane and 67% in Marrakech. The solution that reflects a compromise between the two objective functions (i.e., cooling load and heating load) gives better results in terms of CO2 emissions reduction in all climates evaluated, except in the cold climate.
... This is because, in one hand, building performance simulations has gained a big attention in this field as an alternative to the empirical approach due to their capability to provide adequate conclusions with less time and cost, as well as it enables the designer or engineer to analyze different scenarios during the design phase without the need of existing building. On the other hand, the statistical model combines the speed of simple models and the precision of dynamic simulations if developed adequately [64]. The statistical models are developed by regression techniques from historical data or dynamic models [61], which means that before obtaining the model, we need to collect enough data. ...
The building sector is one of the largest energy end-use sectors in the world and reducing energy consumption has been the foundation of numerous research works. However, the primary objective of buildings must be to provide a comfortable environment for its occupants. Thus, it is necessary to design energy-efficient buildings so that a trade-off between energy-savings and occupants’ thermal comfort is fulfilled. Recently, glass façades have gained popularity due to their aesthetic appearance. However, they often cause occupants thermal discomfort, in addition to consuming considerable amounts of energy. In light of these conflict characteristics, the main purpose of this thesis is to understand the relationship between design parameters, thermal comfort and heating energy, in order to integrate thermal comfort in the design of energy efficient buildings. Consequently, we adopted a methodology based on the combined use of numerical simulations, Design of Experiments (DoE) technique and an optimization method. This allowed the development of meta-models of thermal comfort and heating energy. These models are then used to integrate thermal comfort in the process of building design. A desirability function was considered in order to simultaneously optimize both thermal comfort and heating energy. This trade-off helps in developing an optimal design of buildings at both energy consumption and thermal comfort levels. The proposed method is applied in a real case study. The obtained results show the added value of integrating thermal comfort in building design.
... Some research activities use impact analysis to obtain performance characterization [8,9], while others employ DOE for optimization [10,11]. There are also examples where building simulations were paired with the DOE approach to find the optimal experimental design [12,13] and to develop a simple surrogate model [14]. ...
The two-fold aim of this study was to compare and reflect on the impact of different experimental designs on the characterization of a complex façade system, and to understand the role of constructional elements and boundary conditions on the thermal and fluid dynamic behavior of a double-skin facade (DSF), focusing on the controllability of these phenomena during the operation of the DSF.
We employed and compared four experimental designs capable of assessing factors’ interactions and non-linear behaviors typical of dynamic façades. Experimental data were obtained using a full-scale DSF mock-up, installed in a climate simulator, which was operated in outdoor air curtain mode under boundary conditions typical of the summer season. Similarity and differences between characterizations obtained through different experimental designs enabled us to analyse the impact of different experimental designs and the features that affect the DSF’s performance.
Results demonstrated that the design of experiments methodology could be successfully employed to study the behavior of complex facades. Using more than one experimental design allowed us to obtain a robust picture of the behavior of a naturally ventilated façade. Relevant factors and interactions were also identified and linked to phenomena that determine how the DSF behaves under typical summer conditions.
... By constructing a "typical office building", the researchers characterized the general office in terms of its space, form and thermal properties, which helps to guide energy-efficient design for this type of building. At present, there are two methods to establish a typical building model: the benchmark building method [48] (adopting a survey) and the simplified model method [49] (adopting a fitting formula). This paper used the benchmark building method to create a large office building model according to the literature on the virtual measured data for large office buildings' energy consumption in Beijing, Beijing's existing office building retrofitting demonstration cases and a Chinese building energy conservation statistics report [6,[32][33][34][35][36][37][38][39][40][41][42][43][44]. ...
Energy-efficient retrofitting has emerged as a primary strategy for reducing the energy consumption of buildings. Buildings in China account for about 40% of total national energy consumption. Large office buildings account for the most. Less than 5% of the building area of existing office buildings is energy efficient. Energy-efficient retrofitting for sustainable buildings is a complicated system that involves various sustainable dimensions and operational technical schemes. Making multi-criteria decisions becomes a challenging problem for stakeholders. Based on the theory of sustainability, this paper establishes a sustainable analysis framework to guide stakeholders to select an optimal technical combination of energy-efficient retrofit measures for large office buildings. Based on empirical data collected in Beijing, a number of energy efficiency measures are selected, tailored and applied to a virtual model of a typical large office building. Technical features and the energy performance are simulated accordingly. The energy consumption, energy-saving ratio and lifecycle costs are derived to identify the optimal configuration. The outcome of this research offers a feasible technical plan for stakeholders relating to technical design and design making. The study finds that an LED lighting system and frequency conversion device for the cooling water chiller cannot only sufficiently reduce the building’s energy consumption but also perform economically. Different thermal insulation materials for reconstructing the building envelope have no obvious effect on the thermal performance in comprehensive simulations of technology combinations. The sustainable analysis framework offers theoretical and practical support and can be used as a reference for the other types of buildings in future research.
... ANOVA analysis showed that the main effects were dominant (82.7 %), among which the ceiling had the most substantial influence. Jaffal [36] developed a simple polynomial function using DOE and regression analysis, which estimated the annual energy demand of a low energy building based on its envelope parameters. Simulations were done in TRNSYS. ...
Although a general set of guidelines and procedures for performing the design of experiments (DOE) exists, the literature lacks a recommended course of action for finding and selecting the optimal design of experiments among a large range of possible designs. This research tries to fill this gap by comprehensively testing more than thirty different DOEs through nearly half a million simulated experimental runs. The performance of various DOEs in the characterization of the thermal behaviour of a double skin façade (DSF) is assessed by comparing the outcomes of the different designs and using the full factorial design (FFD) as the ground truth. Besides the finding for the specific case study used in this investigation, this research allowed us to obtain some broad conclusions on the behaviour of different DOEs, which are summarized and translated into recommendations and a general decision tree chart for selecting the suitable DOE(s). The outcomes of this study help researchers and designers to apply DOEs that consider the extent of nonlinearity and interaction of factors in the investigated process in order to select the most successful and the most efficient designs for the specific process characterization.
... In this sense, the use of statistical "Reference Buildings" can support techno-economic optimization studies [65,90], utility scale analysis of design [91] and operation of buildings [92] and energy planning at national scale [68][69][70], where innovative building paradigms are proposed and implemented. In terms of computation, the necessity of performing parametric (or probabilistic) simulation studies [93][94][95]is emerging and the algorithmic definition of simplified building models [96][97][98]can be exploited for building stock modelling at city scale [99][100][101]and regional scale [102]. In Table 3 we synthesize the outcomes of literature analysis regarding building energy performance analysis at multiple scales, highlight the main target of the different studies and their scale of analysis, namely national, regional, urban and stock. ...
Decarbonisation and efficiency goals set as a response to global warming issue require appropriate decision-making strategies to promote an effective and timely change in energy systems. Conceptualization of change is a relevant part of energy transitions research today, which aims at enabling radical shifts compatible with societal functions and market mechanisms. In this framework, construction sector can play a relevant role because of its energy and environmental impact. There is, however, the need to move from general instances to specific actions. Open data and open science, digitalization and building data interoperability, together with innovative business models could represent enabling factors to accelerate the process of change. For this reason, built environment research has to address the co-evolution of technologies and human behaviour and the analytical methods used for this purpose should be empirically grounded, transparent, scalable and consistent across different temporal/spatial scales of analysis. These features could potentially enable the emergence of “ecosystems” of applications that, in turn, could translate into projects, products and services for energy transitions in the built environment, proposing innovative business models that can stimulate market competitiveness. For these reasons, in this paper we organize our analysis according to three levels, from general concepts to specific issues. In the first level, we consider the role of building energy modelling at multiple scales. In the second level, we focus on harmonization of methods for energy performance analysis. Finally, in the third level, we consider emerging concepts such as energy flexibility and occupant-centric energy modelling, considering their relation to monitoring systems and automation. The goal of this research is to evaluate the current state of the art and identify key concepts that can encourage further research, addressing both human and technological factors that influence energy performance of buildings.
... Surrogate models have been used for decades as highperforming function approximators. Examples of commonly used surrogate modeling techniques in BPS include linear regression (Gratia, 2002;Jaffal, Inard, & Ghiaus, 2009;Hygh, Decarolis, Hill, & Ranjithan, 2012;Catalina, Iordache, & Caracaleanu, 2013;Geyer & Schlüter, 2014;Yi, Ritter, 2015), Bayesian networks (Heo, Choudhary, & Augenbroe, 2012;Chong & Menberg, 2018), evolutionary algorithms (Machairas, Tsangrassoulis, & Axarli, 2014), and ANNs (Kalogirou, 2000;New, Ridge, & Parker, 2017;Ascione, Bianco, Stasio, Maria, & Peter, 2017;Singaravel, Suykens, & Geyer, 2018). Since currently available research on and tools for ANNs are not specifically designed to represent architectural geometry and its geometrical relationships, a review of different neural network architectures for representing building geometry in ANN modeling is offered below. ...
Artificial intelligence and data-driven modeling are becoming more prominent in the building, and construction sectors. Physics-based models usually require significant computational power and a considerable amount of time to simulate output. Therefore, data-driven models for predicting the physical properties of buildings are becoming increasingly popular. The objective of this research is to introduce Artificial Neural Networks (ANNs) methods as a means of representing the physical properties of buildings. Achieving this goal will illustrate the future capacity of integrated neural networks in building performance simulations. The Annual Radiation Intensity Neural Network (ARINet) demonstrates the feasibility of using a 3D convolutional neural network to predict the surface radiation received by building façades. The structure of ARINet is composed of 3D convolution, fully connected, and 3D deconvolution layers. In this research, it was trained on 1,692 datasets and validated by 424 datasets generated by a physical simulator. ARINet showed errors in 0.2% of the validation sets.
... In the last piece of paper, the model outcomes were approved with estimated data from 17 squares of flats. Christian Inard et al. [9] utilize the analytical modeling process for the yearly heating demand to foresee the energy pursuance of building as an element of elected criterion by directing a couple of number of simulation tests in order to add to the justification of the design process. F. Sezeret al. [10] analyze the stepwise multiple linear regression analysis is to find the relation between day-by-day mean Total Suspended Particulate (TSP) and Sulphur Dioxide (SO2) combination with meteorological variables, such as temperature, wind speed, pressure in winter seasons. ...
... Today, the importance of parametric and probabilistic analysis of building performance is evident (Jaffal, Inard, & Ghiaus, 2009;Kotireddy, Hoes, & Hensen, 2018;Schlueter & Geyer, 2018;Shiel, Tarantino, & Fischer, 2018), both in new construction and retrofit interventions (EEFIG, 2015;Saheb, Bodis, Szabo, Ossenbrink, & Panev, 2015), and parametric performance data are used for exploratory analysis. ...
Building performance analysis changed the way in which buildings are designed and operated. The evaluation of different design and operation options is becoming more resource intensive than ever before. Although building dynamic simulation tools are potentially a suitable way for assessing energy performance of buildings accurately, they require adequate training and a careful evaluation of model input data. In Europe, the majority of buildings were constructed before 1990 and are in urgent need for a significant energy efficiency improvement, through deep renovation. In this respect, advanced renovation solutions are available, but costly and lengthy renovation processes and technical complexities hinder the achievement of a large scale impact. Energy refurbishment of buildings is an open challenge and essentially requires the adoption of a valid methodological approach to link design and operational performance analysis transparently, in order to address the potential gap between simulated and measured results.
The HEART project, funded in the EU Horizon 2020 program, aims to address the increasing need for deep retrofit interventions and to develop systemic strategies leading to high performance and cost effective solutions. The research for the cloud platform used in the project is based on two fundamental tools: parametric simulation to produce a large spectrum of possible building energy performance outcomes (considering realistically the impact of the user behaviour and variable operating conditions from the very beginning), and model calibration employing simple, robust and scalable techniques. In this paper we present the preliminary development and testing of the computational processes that will be implemented in the cloud platform, employing the first pilot case study of HEART Project in Italy, currently under refurbishment.
... The research work answers to the necessity of linking parametric performance analysis and model calibration from a conceptual and practical point of view. Building performance parametric and probabilistic analysis is an essential tool today to ensure robustness of performance and the importance of the Design of Experiments (DOE) is becoming clear [14][15][16][17], both for new and retrofitted buildings [18,19]. For example, accounting for the robustness of performance estimates with respect to economic indicators (e.g., in cost-optimal analysis [20][21][22]) is important because uncertainty can affect the credibility and, consequently, hinder the success of policies oriented to investments on efficiency in the built environment. ...
High efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of simulation results remains an issue. Further, empirical evidences indicate that the gap between predicted and measured performance can be quite large owing to different types of errors made in the building life cycle phases. Consequently, the discrepancy between a priori performance assessment and a posteriori measured performance can hinder the development and diffusion of energy efficiency practices, especially considering the investment risk. The approach proposed in the research is rooted on the integration of parametric simulation techniques, adopted in the design phase, and inverse modelling techniques applied in Measurement and Verification (M&V) practice, i.e., model calibration, in the operation phase. The research focuses on the analysis of these technical aspects for a Passive House case study, showing an efficient and transparent way to link design and operation performance analysis, reducing effort in modelling and monitoring. The approach can be used to detect and highlight the impact of critical assumptions in the design phase as well as to guarantee the robustness of energy performance management in the operational phase, providing parametric performance boundaries to ease monitoring process and identification of insights in a simple, robust and scalable way.
... Mathematical meta-models help in converting the discretized domain into a continuous one, which leads to improving the accuracy of the near-optimal solution (Stavrakakis et al. 2012). In recent years, design of experiments (DoE) technique has been used with success in the building domain to develop meta-modeling relationships between design factors and response variables (Chlela et al. 2009;Jaffal et al. 2009;Sadeghifam et al. 2015;Hawila et al. 2019). The DoE technique is a methodology for systematically applying statistics to experimentation. ...
In recent years, glass facades and extensive glassing areas have gained popularity in the built environment. However, thermal comfort and energy-savings in such buildings are still questionable. Advanced comfort-based control strategies have been proposed in order to fulfill a tradeoff between energy-saving and occupants’ thermal comfort. Yet, they could consume energy as the conventional control approach if the building is poorly designed. Thus, an adequate design of building envelope, namely glass facades, is essential to achieve the desired trade-off. The objective of this study is to understand and formulate the relationship between the mean radiant temperature (MRT) and glass facades design parameters in a comfort-controlled space in order to optimize building design for a trade-off between energy savings and thermal comfort. The combined use of numerical simulations, the design of experiments (DoE) technique and an optimization approach is adopted. For the investigations, a previously developed and validated dynamic simulation model is used. The combined use of numerical simulation and DoE aims to identify the significant parameters affecting the MRT, as well as to develop a metamodeling relationship between the considered design factors and MRT. Lastly, the developed meta-models are validated and used to determine a set of optimal solutions using the desirability function approach. The results indicate that the optimized design case allowed about 26% reduction of heating energy consumption compared to the base case design. Finally, the results show that an adequate design of the glazed envelope leads to improved thermal comfort conditions and reduce heating energy consumption.
... Meta-model-based optimization eases the burdens of large time and computational costs by replacing the entire simulation process (which requires solutions of transient differential equations) with the evaluation of simple meta-model functions such as polynomials, all while preserving the accuracy of the dynamic simulations. Table 1 compares related research [5,6,11,21]; N [7][8][9][10][12][13][14][15][16][17][18][19][20]22] https Ucar & Balo [9] Energy cost Fuel/insulation material types, wall insulation thickness (9) P1-P2 method, MATLAB O X MATLAB optimization toolbox X 2010 Magnier et al. [10] Thermal comfort, energy consumption Set-temperature, HVAC starting/stopping delay, supply airflow rate, set relative humidity, WAR, thermal mass ( Chantrelle et al. [11] Energy consumption, investment costs, environmental impact External wall, roof, ground Özkan and Onan [12] Energy savings, payback period, insulation thickness Glazing area percentage ( Granadeiro et al. [13] Development of shape grammar-based parametric design system Building width/length, FA, living/service/ porch area, external surface, facade, roof, window area (10) EnergyPlus, ESPr X X Development of shape grammar-based parametric design system X 2015 ...
... In [93, #15] the same authors as in [89] tried to find more generalizable surrogates by integrating know-how on building physics to derive more meaningful features (inputs) from common design parameters. Their features include energy gains due to transmission, air change rate and solar heat gain. ...
Statistical models can be used as surrogates of detailed simulation models. Their key advantage is that they are evaluated at low computational cost which can remove computational barriers in building performance simulation. This comprehensive review discusses significant publications in sustainable building design research where surrogate modelling was applied.
First, we familiarize the reader with the field and begin by explaining the use of surrogate modelling for building design with regard to applications in the conceptual design stage, for sensitivity and uncertainty analysis, and for building design optimisation. This is complemented with practical instructions on the steps required to derive a surrogate model. Next, publications in the field are discussed and significant methodological findings highlighted. We have aggregated 57 studies in a comprehensive table with details on objective, sampling strategy and surrogate model type. Based on the literature major research trends are extracted and useful practical aspects outlined.
As surrogate modelling may contribute to many sustainable building design problems, this review summarizes and aggregates past successes, and serves as practical guide to make surrogate modelling accessible for future researchers.
... These are: pseudo random binary signal (PRBS), sum of sinusoids (SINE) and multilevel pseudo random signal (MPRS). See [35], [36], [38] and [39] to obtain more information about design of experiments. At [37] can be found a comparison between the three signals mentioned above. ...
This Master Thesis compares the simulation and control performance of several black-box and grey-box modeling approaches when using them for model predictive control. A multi-zone dwelling model in developed in Modelica is used as a simulation case to compare the different methodologies.
... The choice of a specific technique can depend on several factors [73]. Further, the proper exploration of design space is crucial and, for this reason, Design of Experiments and parametric design have received an increasing attention in recent years [74,75], consider also Building Information Modelling (BIM) for data standardization [76][77][78]. ...
The transition towards energy systems characterized by high share of weather dependent renewable energy sources poses the problem of balancing the mismatch between inflexible production and inelastic demand with appropriate solutions, which should be feasible from the techno-economic as well as from the environmental point of view. Temporal and spatial decoupling of supply and demand is an important element to be considered for the evolution of built environment, especially when creating sectorial level planning strategies and policies. Energy efficiency measures, on-site generation technologies, demand side management and storage systems are reshaping energy infrastructures and energy market, together with innovative business models. Optimal design and operational choices in buildings are systemic, but buildings are also nodes in infrastructural systems and model-based approaches are generally used to guide decision-making processes, at multiple scale. Built environment could represent a suitable intermediate scale of analysis in Multi-Level Perspective planning, collocated among infrastructures and users. Therefore, the spatial and temporal scalability of modelling techniques is analysed, together with the possibility of accommodating multiple stakeholders’ perspectives in decision-making, thereby finding synergies across multiple sectors of energy demand. For this reason, the paper investigates first the cross-sectorial role of models in the energy sector, because the use of common principles and techniques could stimulate a rapid development of multi-disciplinary research, aimed at sustainable energy transitions. Further, relevant issues for the integration of energy storage in built environment are described, considering their relationship with energy efficiency measures, on-site generation and demand side management.
Understanding the thermal performance of window units is of utmost importance for the advancement of energy-efficient building design. Thermal transmittance (U-factor) is one of the most important indicators of a window system's efficiency, especially in colder climates. Presently, the Window U-factor is typically calculated using one of two standard approaches: experimental methods and computational modeling both having significant limitations. This makes it crucial to develop methods that can calculate the thermal performance of windows in the field and more on already installed windows. This research introduces a data-driven approach to calculating the U-factor of double-glazed windows filled with and without inert gases. The study is confined to double-glazed windows containing either air or a defined percentage of inert gases (75 % Argon, 85 % Argon, and 95 % Argon). The study reveals that the center of glazing, due to its prominence in window construction, plays a significant role in determining the window's thermal attributes. It also establishes a correlation between glass emissivity and center of glass U-factor (thermal transmittance). Furthermore, our developed data-driven approach exhibits considerable reliability in calculating the thermal transmittance of double-glazed windows, as validated by comparison with industry-standard NFRC 100-certified U-factor values. The insights garnered from this research contribute significantly to the understanding of double-glazed window design and the development of more energy-efficient fenestration products. It provides a robust foundation for industry professionals aiming to reduce energy consumption and work towards a sustainable future.
This research book aims to conceptualise the scale and spectrum of Building Information Modelling (BIM) and Artificial Intelligence (AI) approaches in energy efficient building design and to develop its functional solutions with a focus on four crucial aspects of building envelop, building layout, occupant behaviour and heating, ventilation and air-conditioning (HVAC) systems. Drawn from theoretical development on the sustainability, informatics and optimisation paradigms in built environment, the energy efficient building design will be marked through the power of data and BIM-intelligent agents during the design phase. It will be further developed via smart derivatives to reach a harmony in the systematic integration of energy efficient building design solutions, a gap that is missed in the extant literature and that this book aims to fill. This approach will inform a vision for future and provide a framework to shape and respond to our built environment and how it transforms the way we design and build. By considering the balance of BIM, AI and energy efficient outcomes, the future development of buildings will be regenerated in a direction that is sustainable in the long run. This book is essential reading for those in the AEC industry as well as computer scientists.
Energy consumption of buildings accounts for a major share in the modern society. Accurate forecast of building thermal demand is of great significance to both building management systems and heat distribution networks. Machine learning (ML) models driven by abundant naturalistic load data have demonstrated their great capability in estimating real-world consumption patterns and trends. A large number of input features have been considered in the literature for developing data-driven models. However, a thorough analysis regarding their importance is currently lacking. This work first presents a review on the commonly considered features in building thermal demand prediction models, and focuses particularly on their influences. To further facilitate investigating the impacts of various input features, based on a four-year dataset collected from a district heating system with 13 input features, a deep learning model, the long short-term memory (LSTM) network, is employed as a real-world case study. Our results suggest that the past load, outdoor temperature, and hour index have the greatest influence, and should be primarily considered in building thermal demand forecast models. In the case study, they lead to an RMSE of 12.231 MW and a CV-RMSE of 5.814%. Additionally involving wind speed and day index is also useful, which improves the RMSE to 11.971 MW and CV-RMSE to 5.691%. Considering that using all available features achieves the RMSE of only 12.349 MW and CV-RMSE of 5.871%, these five features shall be included to improve the forecast accuracy.
Energy-saving potential prediction models play a major role in developing retrofit scheme. Reliable estimation and quantification of energy saving of retrofit measures for these models is essential, since it is often used for guiding political decision-makers. The aim of this paper is to provide up-to-date approaches of predicting energy�saving effect for building retrofit in large-scale, including data-driven, physics-based, and hybrid approaches, while throwing light on workflow and key factors in developing models. The review focuses on pointing out pivotal aspects that are not considered in current models of predicting energy-saving effect for building retrofit in large-scale. It is concluded that the validation of proposed models mainly focuses on an aggregated level, which
makes it ignore performance gap differences between buildings. The models exist the problem of prebound- and rebound effects due to uncertainty factor. Occupant’s willingness to retrofit is ignored in all three categories of models, which can lead to the prediction result deviate from the actual situation in a certain extent. This paper
promotes the development of models for predicting energy-saving potential for large-scale buildings, and help to formulate appropriate strategies for the retrofit of existing buildings.
Data-driven models have become increasingly prominent in the building, architecture, and construction industries. One area ideally suited to exploit this powerful new technology is building performance simulation. Physics-based models have traditionally been used to estimate the energy flow, air movement, and heat balance of buildings. However, physics-based models require many assumptions, significant computational power, and a considerable amount of time to output predictions. Artificial neural networks (ANNs) with prefabricated or simulated data are likely to be a more feasible option for environmental analysis conducted by designers during the early design phase. Because ANNs require fewer inputs and shorter computation times and offer superior performance and potential for data augmentation, they have received increased attention for predicting the surface solar radiation on buildings. Furthermore, ANNs can provide innovative and quick design solutions, enabling designers to receive instantaneous feedback on the effects of a proposed change to a building’s design. This research introduces deep learning methods as a means of simulating the annual radiation intensities and exposure level of buildings without the need for physics-based engines. We propose the CoolVox model to demonstrate the feasibility of using 3D convolutional neural networks to predict the surface radiation on building facades. The CoolVox model accurately predicted the radiation intensities of building facades under different boundary conditions and performed better than ARINet (with average mean square errors of 0.01 and 0.036, respectively) in predicting the radiation intensity both with (validation error = 0.0165) and without (validation error = 0.0066) the presence of boundary buildings.
The objective of this study is to investigate the capabilities of different global sensitivity analysis methods applied to building performance simulation, i.e. Morris, Monte Carlo, Design of Experiments, and Sobol methods. A single-zone commercial building located in Florianópolis, southern Brazil, was used as a case study. Fifteen inputs related to design variables were considered, such as thermal properties of the construction envelope, solar orientation, and fenestration characteristics. The performance measures were the annual heating and cooling loads. It was found that each method can provide different visual capabilities and measures of interpretation, but, in general, there was little difference in showing the most influent and least influent variables. For the heating loads, the thermal transmittances were the most influent variables, while for the cooling loads, the solar absorptances stood out. The Morris method showed to be the most feasible method due to its simplicity and low computational cost. However, as the building simulation model is still complex and non-linear, the variance-based method such as the Sobol is still necessary for general purposes.
Fully defined physics-based building energy models can accurately represent building systems; however, generating models based on high-level parameters is time consuming and simulation time of complex models can be slow. This article discusses the development of a Metamodelling Framework to create metamodels from a building energy modelling dataset. The framework generates metamodels using either linear regression, random forests, or support vector regressions. A fifth-generation district heating and cooling system analysis use case was used to motivate the development of the framework. The use case required quick and accurate representations of annual building loads reported hourly. Typical annual building modelling approaches can result in a runtime of 10 min. The metamodels runtime was reduced to less than 10 s to load and run an annual simulation with user-defined covariates. The results of the metamodel performance and an abbreviated topology analysis based on the motivating use case will be presented.
A considerable amount of the oil used in Saudi Arabia is consumed to generate power. In an effort to minimize such consumption, the government of Saudi Arabia has established an ambitious part of the 2030 Vision that includes 96 strategic objectives. In recent years, the advancement in information technology has made it possible to develop accurate prediction models of energy consumption for constructed facilities, which can assist with establishing plans to reduce future energy consumption. This research proposes a regression-based model for predicting the energy consumption of one of the most significant energy-consuming types of facilities in Saudi Arabia: schools. The model was developed utilizing 350 actual data points of energy consumption gathered from schools operating in the eastern province of Saudi Arabia. The factors affecting consumption of energy were identified from two sources. The first was a review of the literature, and the second was interviews with local experts. A sensitivity analysis indicated that the most important factors (i.e., inputs/independent variables) affecting energy consumption (i.e., output) were AC capacity and building age. An investigation of the correlations among the independent variables revealed that the highest correlation of 0.864 was found between the total built area and total roof area. The developed model was validated using 35 new data points of school buildings across the eastern province of Saudi Arabia. The results show that the model predicted the energy consumption of school buildings with an accuracy higher than 90%. The findings of this study will assist schools and maintenance managers in effectively managing such facilities by allowing them to allocate the required budget in advance. Also, the predictions can be used economic lifecycle analysis.
In most of the countries, buildings are often one of the major energy consumers, leading to the necessity of achieving sustainable building designs, and to the mandatory use of building performance simulation (BPS) tools in order to retrofit or design new energy efficient buildings. In the last years, the use of artificial neural networks (ANNs) metamodels has increased and gained confidence in BPS applications thanks to their favorable trade-off between accuracy and computational cost. This paper presents a comprehensive and in-depth systematic review of the up-to-date literature related to the application and characterization of ANN-based metamodels for BPS. First, a general insight into the methodology of metamodel generation and ANN theory is presented. The ANN metamodels are classified according to the type of building they are addressed to, screening them by their inputs (building design variables or indicators to take a certain decision) and outputs (energy consumption, comfort index, climatic condition, environment performance). Then, all the stages for the generation of ANN-based metamodels (sampling methods, data pre-processing, architectures, activations functions, the process of training and testing, and the platforms and frameworks for their implementation) are presented giving a brief theoretical introduction and making a critical review of the literature linked to each stage. For each of these analyzed stages, summary tables and graphs are presented showing the distributions of different alternatives and trends. Finally, the current limitations and areas for further investigation are discussed.
The early building design is an iterative process. In this process, architects and engineers evaluate different design concepts to ensure the design brief is fulfilled. The rising need for a design to adhere to certain performance has introduced building performance simulation (BPS) into the design process. Early design decisions have the highest impact on building performance. Therefore, it is important to make good design decisions at this stage of the design process.
The computational effort, in terms of model development time and prediction time, is high for BPS. The computational effort combined with other factors like the need for detailed design information limits BPS in the early design stages. This research focusses on having computational methods that deliver the predictions as fast as possible. The speed to obtain a prediction is important for iterative early design, as the prediction models should be able to keep up with the thinking speed of a designer. For example, simple BPS takes about 5 minutes to make a prediction on design performance. Five minutes may appear like an insignificant amount of time; This time accumulates as more design needs to be evaluated. Moreover, during the creative process of a designer, his or her speed of iterating over design options is faster than a BPS model’s time to provide performance results. The slow nature of BPS results in limited design options evaluated for building performance and potentially missing design with ideal performance.
Furthermore, computational speed becomes a challenge when designers with different educational backgrounds need to collaborate on a design. The low computational speed of BPS makes designers rely more on rule-of-thumb knowledge. The applied rule-of-thumb knowledge may or may not be valid for the proposed design problem. It is increasing the risk of not taking the right design decisions from a performance point-of-view. To overcome the challenge of computational speed, this research evaluates machine learning (ML) as an alternative method for building performance prediction. Reason for using ML is its high computational speed and prediction accuracy. However, ML models have to overcome challenges like generalization, reusability, and interpretability.
This research evaluates generalization through two different approaches, which are component-based approach and deep learning. Both approaches model the relationship between building design parameters and design performance in hierarchies. Results indicate that ML models do generalize in unseen design cases, provided the evaluated design is similar to the nonlinearity present in the training data distribution. Within the ML algorithms, deep learning model architectures based convolutional neural networks (CNN) outperform traditional neural networks (NN). CNN is able to outperform traditional NN as it could extract features from the data in a hierarchical manner.
The reusability of the ML model is evaluated to reduce the computational effort required in developing multiple ML models. Transfer learning and multi-task learning methods are evaluated to understand ML model reusability. Developing1 two deep learning models sequentially takes ~22 minutes. This development time accumulates as the number of models to be developed increases. Results indicate that through both transfer learning and multi-task learning computation effort for model development can be reduced without compromising on model accuracy. The development times are reduced to ~14 minutes and ~8 minutes respectively.
The interpretability of deep learning methods is evaluated through dimensionality reduction methods. Results indicate that deep learning models learn to re-organize the design space based on the design’s energy signature. Therefore, the trained model is similar to a top-down approach for predictions, in which, predictions are based on design similarity.
Finally, results show that ML models predict design performance for 201 design options in 0.9 seconds, while the same results can be obtained from BPS in ~20 minutes. Showing that ML models are significantly faster than BPS. Results are giving an indication that ML models could indeed keep-up with the speed of a designer. The thesis elaborates more on the ML methods evaluated and the outcomes of the research.
Understanding the nonlinearity of the thermal behavior of buildings is important for their design and energy analysis. This paper presents a method for the study of the nonlinearity of building thermal behavior based on a metamodel for cooling energy needs. Four measures were introduced to assess nonlinearities using the metamodel coefficients. We studied the nonlinearity of the thermal behavior of an office. A higher metamodeling accuracy was generally obtained for hot climates, high internal heat gains and lightweight thermal mass. Conversely, the nonlinearity of thermal behavior was accentuated in cold climates and with low internal heat gains. The nonlinearity measures were strongly associated to the mean outdoor air temperature in fifteen typical European climates using power laws, with R² ranging from 0.75 to 0.96. They were also strongly associated to internal heat gains (R² > 0.96) in the coldest climate, but low and almost stable in the hottest climate. Moreover, the interactions between the building components were more influential on cooling energy needs than quadratic behavior. A metamodel giving energy needs as a function of the physical and geometric parameters was derived. Its extrapolation with various window-wall ratios generally gave an acceptable accuracy, with quadratic variation of the errors. We propose a classification of building thermal behavior into three regimes: highly nonlinear when the energy needs are close to zero; intermediate with decreasing nonlinearities that can be expressed by power functions; and finally, a quasi-linear regime with almost-steady nonlinearities.
In this study, a metamodel for cooling energy consumption is presented. In constructing the metamodel, the assumption was made that cooling energy consumption is a polynomial function of the energy consumption of each individual building component. We studied the cooling energy consumption of an office under various climate conditions and with different thermal masses and uses. The metamodel was fitted from dynamic simulations by multiple regression analysis and the accuracy obtained was very close to that of dynamic simulation. Our study highlights the value in using the Design of Experiments method to fit the metamodel since the number of simulations could be reduced without a loss of accuracy. The metamodel coefficients were analysed to gain insight into the thermal behaviour of a building. We found that the energy consumption for cooling was a convex function of the individual energy consumptions of the building components. The results suggest that it would be beneficial to study third order and exponential terms. This work opens a path to a new way of analyzing building energy performance.
The primary objective of buildings must be to provide a comfortable environment for people. Recently, glass facades have gained popularity due to their aesthetic appearance. However, Low performance facades often allow substantial heat exchange between the indoor and outdoor environment that increases building energy consumption and rapid change in indoor thermal environment near the glass façade. Thus, adequate design of building envelope, namely glass facades, is essential to ensure a trade-off between several aspects, such as aesthetic appearance of the building, occupants’ thermal and visual comfort and energy consumption. The main purpose of this study is to quantify the interactions and optimize building design, particularly glass facades, for thermal comfort based on the combined use of numerical simulations, Design of Experiments (DoE) technique and an optimization method. The proposed approach is applied to a real case study, characterized by two glass facades, after subjectively assessing thermal comfort using survey questionnaire. For the analysis, a previously developed and validated dynamic simulation model is used. The combined use of numerical simulations and DoE aims to determine the critical parameters affecting thermal comfort, and to develop meta-modeling relationships between design factors and response variables. The developed meta-models are then used to determine a set of optimal solutions by performing a simultaneous optimization of building design based on the desirability function approach. The results indicate that the optimized design improve thermal comfort conditions as well as energy-savings. Finally, the results show the added value of the proposed methodology towards enhanced thermal comfort conditions.
This study aims at predicting, through mathematical models, the thermal loads of a building's cell in Marrakech (Morocco), whose climate is hot semi-arid. Eight building's parameters were selected to model its energy demand using multiple linear regression method that lead to high accuracy mathematical models. These models are built from the results of several TRNSYS dynamic simulation. The analysis of the results allows concluding that, in hot semi-arid climates, the building's parameters that have high impact on its energy loads are mainly the window-to-wall ratio, the roof and the walls thermal insulation. The control of these three parameters can improve more than 92% and 87% of the building's cooling and heating loads respectively.
Full text Available at:
https://ieeexplore.ieee.org/document/8477397
Efficient buildings are an essential component of sustainability and energy transitions, which represent today a techno-economic and socio-economic problem. New paradigms are emerging both for new and existing buildings (e.g. NZEBs) and passive design strategies are becoming increasingly common. However, the adoption of these strategies in mild climates has to be carefully evaluated to prevent overheating in intermediate seasons and increasing cooling loads in summer, considering also climate change scenarios. Additionally, optimistic assumptions about building technology performance are often considered and the variability of occupant comfort preferences and behaviour is generally neglected in the design phase. The research presented aims at verifying the suitability of a simple, robust and scalable calibration approach (based on multivariate linear regression) to link design and operational performance analysis transparently, using a Passive House case study building. First, the original baseline design configuration is compared with a larger spectrum of data generated by means of parametric simulation, following a Design of Experiment (DOE) approach. After that, regression models are trained first on simulation data and then progressively calibrated on measured data during a three year monitoring period. The two fundamental objectives are evaluating the robustness of design phase performance analysis through parametric simulation (i.e. detecting potentially critical assumptions) and maintaining a continuity with operation phase performance analysis (i.e. exploiting the feed-back from measured data).
L'objectif des travaux présentés dans ce mémoire concerne le développement d'un modèle global représentant le couplage de l'enveloppe du bâtiment avec les équipements énergétiques. Une approche systémique appelée les Bond Graphs, peu employée jusqu'ici dans la modélisation des systèmes thermiques, est utilisée. Le modèle global du bâtiment, regroupant sous le même environnement de simulation, les modèles de l'enveloppe du bâtiment, les apports solaires, les émetteurs de chauffage et de rafraîchissement et le système de ventilation, est développé pour reconstituer l'ensemble des articulations énergétiques entre l'enveloppe et les environnements intérieur et extérieur. A travers la modélisation d'un bâtiment multizone, le couplage systémique des modèles de l'enveloppe et des apports solaires est présenté. Par ailleurs, un système combinant un plancher chauffant et un plafond rafraîchissant est étudié à l'aide des modèles des émetteurs de chauffage et de rafraîchissement. Le renouvèlement d'air dans le bâtiment est également concerné par la modélisation Bond Graph. Enfin, des éléments de validation expérimentale sont présentés. Pour cela, la plateforme de tri-génération d'énergie ENERBAT est exploitée. L'objectif est d'étudier le couplage optimal enveloppe du bâtiment - équipements énergétiques pour lequel les modèles BG sont développés. Une étude paramétrique tenant compte des interactions entre les paramètres étudiés est menée sur un projet réel de rénovation. Finalement, une combinaison appropriée des paramètres étudiés a été retenue afin de réduire la consommation énergétique selon la réglementation thermique française (RT2012)
La plupart des ingénieurs et techniciens améliorent leurs produits ou leurs processus de production à partir d'essais. Malheureusement, les stratégies couramment utilisées pour mener ces expériences sont souvent informelles et peu performantes. Elles conduisent à de nombreux essais difficiles à exploiter. La méthode des plans d'expériences permet d'adopter une démarche formelle pour réaliser des essais qui évite les coûteux inconvénients d'une stratégie de tâtonnement Cet ouvrage a été conçu pour permettre au professionnel d'aborder les concepts de base sur les plans d'expériences et de réaliser très rapidement ses premiers plans. L'accent a été mis sur une approche pédagogique des plans d'expériences notamment dans les premiers chapitres volontairement dépourvus de calculs statistiques afin de permettre au lecteur de se consacrer à l'essentiel. Les premiers chapitres de l'ouvrage sont consacrés aux plans d'expériences complets qui sont très faciles à mettre en œuvre. Les chapitres suivants abordent les plans fractionnaires en utilisant la démarche du Docteur Taguchi qui simplifie considérablement la création d'un plan d'expériences. Des stratégies originales sont proposées afin d'élaborer des plans d'expériences en limitant les risques pour l'expérimentateur. Les professionnels chevronnés pourront affiner leur pratique en abordant la démarche de conception robuste fondée sur les plans produits de Taguchi. Un chapitre important est consacré à l'optimisation des systèmes dynamiques dont l'efficacité est redoutable.
The potential offered by computer simulation is of- ten not realized: Due to the interaction of system vari- ables, simulation users rarely know how to choose input parameter settings that lead to optimal perfor- mance of a given system. Thus, a program called GenOpt that automatically determines optimal pa- rameter settings has been developed. GenOpt is a generic optimization program. It min- imizes an objective function with respect to multi- ple parameters. The objective function is evaluated by a simulation program that is iteratively called by GenOpt. In thermal building simulation - which is the main target of GenOpt - the simulation pro- gram usually has text-based I/O. The paper shows how GenOpt's simulation program interface allows the coupling of any simulation program with text based I/O by simply editing a configuration file, avoiding code modification of the simulation program. By us- ing object-oriented programming, a high-level inter- face for adding minimization algorithms to GenOpt's library has been developed. We show how the algo- rithm interface separates the minimization algorithms and GenOpt's kernel, which allows implementing ad- ditional algorithms without being familiar with the kernel or having to recompile it. The algorithms can access utility classes that are commonly used for min- imization, such as optimality check, line-search, etc. GenOpt has successfully solved various optimiza- tion problems in thermal building simulation. We show an example of minimizing source energy con- sumption of an office building using EnergyPlus, and of minimizing auxiliary electric energy of a solar do- mestic hot water system using TRNSYS. For both ex- amples, the time required to set up the optimization was less than one hour, and the energy savings are about , together with better daylighting usage or lower investment costs, respectively.
Simply minimising the heat loss from a building will not necessarily lead to an exemplary low-energy design: overheating may occur, leading to a large amount of cooling energy being used, and the shape and form of the design may not fit with other sensitivities and elements of the design brief. This paper couples a population-based optimisation algorithm (a genetic algorithm) to a dynamic thermal model with the idea of identifying large numbers of distinctly different low-energy designs. These designs are then presented to the user in the form of a visual summary for judgement as to potential use.In order that sufficiently different designs are evolved, and the thermal model can be run over a complete year on an hourly grid, several adaptations to the genetic algorithm have had to be made.The approach is illustrated by the design of a community hall. An extensive range of design possibilities is identified which achieve low-energy status by greatly different means with some concentrating on reducing losses and others on maximising their use of causal gains, including solar gains.
Developers of dynamic building simulation programs generally use tests to validate software but there is not a systematic, consistent approach to testing. This paper examines the history and development of the tests used to validate the ESP-r dynamic building simulation software, and reviews the benefits of embedding validation tests in simulation models to allow routine checking of changes to the programming. It also presents the ideas behind tests developed for program users by the UK's Chartered Institution of Building Services Engineers. Les concepteurs de programmes de simulation dynamiques de bâtiments valident généralement les logiciels sur la base d'essais mais il n'existe pas de méthode d'essai systématique et uniforme. Cet article examine l'historique et le développement des essais utilisés pour valider le logiciel de simulation dynamique de bâtiment ESP-r; il passe en revue les avantages d'intégrer les essais de validation aux modèles de simulation afin de permettre une vérification systématique des modifications de la programmation. Il présente aussi les idées à l'origine des essais développés pour les utilisateurs du programme par le Chartered Institution of Building Services Engineers du RU. RES
This study uses the TRNSYS computer program, for the modelling and simulation of the energy flows of modern houses, to examine measures to reduce the thermal load. For the calculations, a typical meteorological year (TMY) and a typical model house are used. The measures examined are natural and controlled ventilation, solar shading, various types of glazing, orientation, shape of buildings, and thermal mass. In summer, ventilation leads to a maximum reduction of annual cooling load of 7.7% for maintaining the house at 25 °C. The effect depends on the construction type, with the better-insulated house saving a higher percentage. Window gains are an important factor and significant savings can result when extra measures are taken. The saving in annual cooling load, for a well-insulated house, may be as much as 24% when low-emissivity double glazing windows are used, which are recommended since the payback period is short (3.8 years). Overhangs may have a length over windows of 1.5 m. In this way, about 7% of the annual cooling load can be saved for a house constructed from single walls with no roof insulation. These savings are about 19% for a house constructed from walls and roof with 50 mm insulation. The shape of the building affects the thermal load. The results show that the elongated shape shows an increase in the annual heating load, which is between 8.2 and 26.7% depending on the construction type, compared with a square-shaped house. Referring to orientation, the best position for a symmetrical house is to face the four cardinal points and for an elongated house to have its long side facing south. In respect to thermal mass, the analysis shows that increasing the wall and roof masses and utilizing night ventilation is not enough to lower the house temperature to acceptable limits during summer. Also, the analysis shows that the roof is the most important structural element of the buildings in a hot environment. The roof must offer a discharge time of 6 h or more and have a thermal conductivity of less than 0.48 W/mK. The life-cycle cost analysis has shown that measures that increase the roof insulation, pay back in a short period of time, between 3.5 and 5 years. However, measures taken to increase wall insulation pay back in a long period of time, of about 10 years.
Buildings use energy for heating, cooling and lighting, contributing to the problems of exhaustion of fossil fuel supplies and environmental pollution. In order to make buildings more energy-efficient an extensive set of âenergy saving building componentsâ has been developed that contributes to minimizing the energy need of buildings, that helps buildings to access renewable energy sources, and that helps buildings to utilize fossil fuels as efficiently as possible. Examples of such energy saving building components are heat pumps, sunspaces, advanced glazing systems, thermal insulation layers, etc. Building simulation tools appear to be a suitable instrument to support decisions regarding the selection and integration of energy saving building components: they can provide detailed information on the thermal performance of buildings that have not yet been built, thereby allowing objective comparison of different design options under identical conditions. However, in general the actual use of simulation tools to provide information to support the selection of energy saving building components does not live up to this expectation. The development of new building energy simulation tools shows a continuous increase of capabilities and complexity. This trend increases the dependency on adequate modeling and expertise, and thereby increases the barriers to integration of building design process and building simulation even further. Therefore, the central goal of the PhD-project is the development of a strategy to provide computational support during the building design process for rational design decisions regarding the selection of energy saving building components. The strategy is to be substantiated by development of a prototype that demonstrates the feasibility of the strategy. The work presented in this thesis consists of four main research activities, all focusing on the use of simulation tools to support the selection and implementation of energy saving building components: 1) analysis of the design process of current energy-efficient building projects; 2) development of an approach for well-founded selection of these components; 3) analysis of the suitability of existing tools to support the selection process, and development of ideas for improvement of these tools; 4) development of a strategy as well as a proof-of-concept prototype that provides support for the selection of energy saving components and that demonstrates the viability of the proposed changes. Analysis of current energy-efficient building projects The analysis of current energy-efficient building projects was initiated by a lack on unbiased information on the way in which energy saving building components are selected in current practice, and lack on information of the role of simulation tools in this selection process. The goal of the analysis was to find out for recent prestigious building design projects in the Netherlands how this selection took place, and what role tools played in supporting the selection. In order to attain this goal three case-studies and a survey were conducted. The case-studies provided in-depth information on three projects; the survey demonstrated the representative ness of the findings from the case-studies for a larger sample of energy-efficient buildings. The overall findings are that in current projects simulation tools do not play an important role in the selection of energy saving building components, since these tools are used in later phases than those relevant for the selection, and are only used for different purposes (optimization and verification rather than to support choices). Instead, most energy saving building components are selected based on analogy: use of similar components in previous buildings by the architect or consultant, or the use of these components in demonstration projects. It appears that decision-making on energy saving building components is based on simple, heuristic decision rules. Yet it seems preferable to apply multi-criteria decision rules to the selection of these components, ensuring that different requirements are considered in the decision-making process. Hence there is a need to improve both the selection procedure as well as the tools that support that selection. An approach for well-founded selection of energy saving building components The development of an approach for well-founded selection of energy saving building components had as goal to improve the current way of selecting these components. Requirements and constraints for making well-founded choices have been identified and used to assess existing theories for making design decisions. An approach for performance-based selection of energy saving building components has then been developed, using applicable elements from existing theories to define the essential steps: definition of an option space, identification of relevant functions, specification of performance indicators, prediction of performance for all options and all performance indicators, and evaluation followed by selection of the most desirable option. This approach rationalizes the selection procedure, and makes the role of subjective assessment explicit. Since it is based on performance prediction, it provides an optimal base for the use of simulation tools. The viability of this approach has been demonstrated through application of the approach to an example. Analysis and improvement of tools Once the selection procedure had been developed, the next goal was to improve the tools that support this procedure. The analysis and improvement of tools for the selection of energy saving building components consisted of the following steps: analysis of the different main categories of tools (design tools, modeling tools, analysis tools, support environments and others) and their role in supporting the selection of energy saving building components, and assessment of existing tools as well as identification of possibilities for improvement of the two most important categories (analysis tools and support environments). It was found that existing analysis tools are capable of supporting the selection according to the performance-based approach, on condition that enough time and expertise is available for the modeling and simulation work. Support environments are mostly still under development and have not yet gained widespread use. Analysis tools can be improved through reverse-engineering, which clarifies the building design alternatives and performance indicators that can be handled by these tools. Support environments can be improved by embedding analysis tools as well as a selection mechanism that helps users to find a suitable (analysis) tool for any specific (analysis) job. A strategy and prototype for the selection of energy saving components The final goal of the research project was the development of the strategy to provide computational support during the building design process for rational design decisions regarding the selection of energy saving building components, and the realization of a substantiating prototype that shows the viability of this strategy. In order to reach this goal the afore-mentioned ideas on improvement of the process and support tools have been combined. Participation in an international research project, the Design Analysis Interface (DAI) - Initiative, provided the final elements needed for completion of the research. A strategy for selection of energy saving building components has been developed in this thesis that consists of the following elements: 1. Energy saving building components should be selected according to a procedure that consists of definition of an option space, identification of relevant functions, specification of performance indicators, prediction of performance for all options and all performance indicators, evaluation of predicted performance and selection of the most desirable option. 2. Availability of time and expertise for modeling and simulation work are the most important limiting factors that hinder the application of existing building performance simulation tools in support of the selection of energy saving building components. In order to overcome this problem the analysis request must be stated unambiguously. At the same time, building performance simulation tools must be pre-conditioned (reverse-engineered) in order to meet these specific analysis requests. 3. The procedure for the selection of energy saving building components must be assisted by the use of a support environment that provides a mechanism that gives users access to different (embedded) building performance simulation tools for doing specific analysis tasks. A prototype of a Design Analysis Interface (DAI) - Workbench has been developed that demonstrates the feasibility of better integration of building analysis tools and building design process through the use of a layered, process-centric approach, thereby showing the viability of the ideas to provide improved computational support for the selection of energy saving building components. The concept of analysis functions links the analysis process with simulation tools by matching analysis task and tool capabilities. An analysis function gives an exact specification of the performance indicator that is to be generated by the analysis. Of course, full computational support for the selection of energy saving building components can only be achieved once the DAI-Workbench contains a set of analysis functions that covers most relevant performance aspects for buildings with such components, plus qualifying tools and interfaces from analysis functions to those tools. Future work on the integration of building simulation and building design requires further development of support environments that capture and support the analysis process itself, and that provide access to tools that are able to support relevant process steps. Reverse-engineering of simulation tools to match specific analysis tasks seems an important task in order to increase the applicability of these tools.
The methods used to assess the energy performance of buildings are static or dynamic. A paradigm shift is to estimate the energy consumption by using a probabilistic approach and a single concept for the whole range of operation of the building (heating, ventilation and cooling) and to decouple the thermal behavior of the building, the thermal comfort range and the climate data. These requirements can be achieved by using the free-running temperature to characterize the building behavior and the frequency distribution of the outdoor temperature to describe the climate. This paper demonstrates that the dynamic values can be used to estimate the energy load curve, that this curve may be applied to calculate the energy consumption and that the free-running temperature is an equivalent form of the heating/cooling curve. The main advantages of using the concept of free-running temperature are that: (1) the dynamic behavior may be described by steady-state concepts, (2) the whole range of building operation (heating, ventilation and cooling) is described by a single concept and (3) the thermal behavior of the building, the comfort and the climate are decoupled. The mathematical formalism uses matrix notation.
Estimation of energy performance indexes, like the heating curve or the energy signature, requires robust regression of the heating losses on the outdoor temperature. The solution proposed in this paper is to use the range between the 1st and the 3rd quartile of the quantile–quantile (q–q) plot to check if the heating losses and the outdoor temperature have the same distribution and, if yes, to perform the regression in this range of the q–q plot. The result is a model that conserves its prediction performance for data sets of the outdoor temperature different of those used for parameter identification. The robust model gives the overall heat transfer coefficient and the base temperature, and it may be used to estimate the energy consumption for data sets of the outdoor temperature coming from different time—space locations.
The purpose of this article is to present the method of solving the optimization problem of the internal partitions of the building, the shape of the building, as well as heat sources.The optimization of the internal partitions of the building is based on particular selection from the given list, and determination of the thickness of thermal insulation.Solving the problem of the shape has been solved first in order to determine approximately the building height, proportions of building sides and orientation of the building with respect to the north–south axis, as well as to check which of the inequality constraints are active in this case. Following this, after obtaining the results, it was checked whether replacing the rectangular plan of the building by a rectangle and two trapezoids is advantageous.The optimum choice of heat source types for heating for domestic use and the determination of share in covering the demand for heat in the time interval under consideration has been formulated and solved using continuous and discrete decision variables.Part-problems of the optimization are solved by analytical–numerical method. They consist of determining some decision variables using analytical methods and the remaining using numerical methods applying CAMOS computer system, original algorithms and programs. In optimization of heat sources, original numerical methods were used for the determination of the compromise set in case of discontinuous objective functions.
The purpose of this paper was to illustrate the example of multicriteria optimization of the big blocks of flats. The method described in [Hwang and Masud (Multiple objective decision making—methods and applications—a state-of-art-survey. Lecture notes in economics and mathematical systems, Springer, Berlin, 1979)] and [Jendo and Marks (Archives of Civil Engineering 30(1) (1984))] is based on decomposition into part-problems: optimization of internal partitions of the building, the shape of building, heat sources, and finally adequate co-ordination of solutions. The above-mentioned co-ordination is obtained by an iterative method.
This paper summarizes a PhD-project that is currently under completion at Delft University of Technology, Faculty of Architecture, Building Physics Group. The general problem addressed in this project is the integration of building simulation tools and building design. This problem has been narrowed down to computational support for one specific type of building design decision: the selection and integration of one or more energy saving building components like solar walls, advanced glazing systems, sunspaces and photovoltaic arrays into a given building design.The paper provides an overview of the main research efforts that were carried out during the research [Computational Support for the Selection of Energy Saving Building Components, PhD thesis (in preparation), Building Physics Group, Faculty of Architecture, Delft University of Technology, 2004]. Based on results and on views that have been described in earlier publications (e.g. [Beter ontwerp door gebouwprestatie-simulatie? in: Proceedings of the Third IBPSA-NVL Conference, Petten, The Netherlands, December 2001]), the paper then will identify relevant, challenging issues for future research in the area of integration of building simulation tools into the building design process. Specific attention will be paid to issues related to the use of different categories of tools (simulation tools as well as other instruments) for the development of new, energy-efficient building design concepts.
This paper describes TRNSYS, a computer program designed specifically to connect component models in a specified manner, solve the simultaneous equations of the system model, and display the results. Solar energy system components are described by individual FORTRAN subroutines. These subroutines comprise a growing library of equipment models available to the user for system simulation.
Residential oil burners are capable of almost complete burning of the fuel oil, without visible smoke, when they are operated to deliver approximately 12% CO2 in the flue gases. The positions of the air damper and of the combustion nozzle are adjusted at start-up and during operation in order to maximize the combustion efficiency. In practice, one factor at a time is varied, starting with the air damper. However, this method fails to detect the interaction between air excess and nozzle position and results in non-optimal settings. Optimal designed experiments allow obtaining local regression models and statistical analyses indicate if experiment augmentation is required. The air damper and combustion nozzle settings are changed in the direction of local gradient until a second order model that contains the optimal point in its experimental region is obtained. The gain in combustion efficiency thus obtained may be up to 5% as compared with the classical approach.
This paper provides a simplified analysis method to predict the impact of the shape for an office building on its annual cooling and total energy use. The simplified analysis method is developed based on detailed simulation analyses utilizing several combinations of building geometry, glazing type, glazing area and climate. A direct correlation has been established between relative compactness and total building energy use as well as the cooling energy requirement.
A great amount of world energy demand is connected to the built environment. Electricity use in the commercial buildings, accounts for about one-third of the total energy consumption in Turkey and fully air-conditioned office buildings are important commercial electricity end-users since the mid-1990s. In the presented paper, the interactions between different conditions, control strategies and heating/cooling loads in office buildings in the four major climatic zones in Turkey – hot summer and cold winter, mild, hot summer and warm winter, hot and humid summer and warm winter – through building energy simulation program has been evaluated. The simulation results are compared with the values obtained from site measurements done in an office building located in Istanbul. The site-recorded data and simulation results are compared and analyzed. This verified model was used as a means to examine some energy conservation opportunities on annual cooling, heating and total building load at four major cities which were selected as a representative of the four climatic regions in Turkey. The effect of the parameters like the climatic conditions (location), insulation and thermal mass, aspect ratio, color of external surfaces, shading, window systems including window area and glazing system, ventilation rates and different outdoor air control strategies on annual building energy requirements is examined and the results are presented for each city.
The Mediterranean climate is characterized by a high level of the solar resource in winter and some coolness of the nights in summer, which offer a good opportunity for thermal comfort achievement at low energy cost and reduced CO2 emissions, provided that an appropriate design of the building envelope is adopted. It has thus been decided to implement some regulations in three countries of North-Africa for controlling the heating and cooling loads. The study described in this paper has been conducted in the frame of this project. It has two objectives: to define adequate indicators for the thermal performance of buildings during both the cold season and the hot one, and to develop a standard calculation procedure for these indicators. The developed procedure, CHEOPS, is fast and requires minimum input data; it is very easy to use, the steps for calculating the cooling and heating coefficients being almost the same. The transmission losses and the solar gains are clearly identified; this fact can contribute to a better understanding by the designer of the effect of each parameter, leading him towards the appropriate trade-offs between summer and winter considerations.
The aim of the paper is to present rational methods of multicriteria optimization of the shape and structure of energy-saving buildings, as well as optimization of heat sources taking into account the energy criteria.Mathematical model describing heat losses and gains in a building during the heating season was selected and refined. It takes into consideration heat losses through walls, roof, floor and transparent partitions as well as heat gains due to insolation across transparent partitions. Particular attention was paid to a more detailed description of heat gains due to solar radiation, depending on the position of partitions relative to their north–south axis and the degree of their use.The problem of multicriteria optimization of individual homes and blocks of flats was formulated on the basis of the following criteria: •minimum construction costs, including cost of materials, erection, heating installations together with the cost of heat sources,•minimum seasonal demand of heating energy,•minimum of pollution emitted by heat sources installed in the building.The following were accepted as the global optimization criteria allowing to find the preferred solution: •minimum building construction costs and its running costs over N-years’ period together with minimum environment pollution during that period,•minimum distance from the point belonging to the compromise set to the ideal point.The method of solving the above formulated problem consists in decomposing it into three part-problems: •optimization of internal partitions of the building,•optimization of the shape of building,•optimization of heat sources.
Part-problems must be co-ordinated subsequently.
We present the energy use situation in Hong Kong from 1979 to 2001. The primary energy requirement (PER) nearly tripled during the 23-year period, rising from 195,405 TJ to 572,684 TJ. Most of the PER was used for electricity generation, and the electricity use in residential buildings rose from 7556 TJ (2099 GWh) to 32,799 TJ (9111 GWh), an increase of 334%. Air-conditioning accounted for about 40% of the total residential sector electricity consumption. A total of 144 buildings completed in the month of June during 1992–2001 were surveyed. Energy performance of the building envelopes was investigated in terms of the overall thermal transfer value (OTTV). To develop the appropriated parameters used in OTTV calculation, long-term measured weather data such as ambient temperature (1960–2001), horizontal global solar radiation (1992–2001) and global solar radiation on vertical surfaces (1996–2001) were examined. The OTTV found varied from 27 to 44 W/m2 with a mean value of 37.7 W/m2. Building energy simulation technique using DOE-2.1E was employed to determine the cooling requirements and hence electricity use for building envelope designs with different OTTVs. It was found that cooling loads and electricity use could be expressed in terms of a simple two-parameter linear regression equation involving OTTV.
Buildings complying with the Passive House standard are rapidly spreading across Germany, Austria and Switzerland. The underlying Passive House concept is based on a holistic approach, improving the building envelope to a degree that allows for substantial simplifications of the heating system. Passive Houses offer increased comfort at affordable costs while significantly reducing the energy consumption. The useful energy required for space heating has been reduced by ca. 80% compared with conventional (new) buildings. The overall primary energy consumption (including all services and electric appliances) has been reduced by more than 50%. Our paper introduces the Passive House standard and summarizes results of the EU project ‘Cost Efficient Passive Houses as European Standards’ (CEPHEUS) with respect to energy indices and comfort. Characteristics of the combined ventilation and heating system realized in many Passive Houses are presented in detail, including results of an expert working group at the Passive House Institute and on measurements and simulations conducted at EMPA. Chances and limitations of wood stoves as supplementary heat sources in Passive Houses are discussed shortly.
The objective of the OPTI program (the office building module) is to help architects, engineers and design departments to take into account the impact of design choices on energy consumption designing a project.In order to do so, the program must:•be highly user-friendly (language based on drawings);•need a minimum of data;•be very fast.The computation of energy needs and overheating estimation require dynamic thermal programs. These are often slow and need a large amount of data.The use of many parametric studies realized with a dynamic thermal program along with the application of a method used in experimental research (method of experimental design) allowed us to create software providing the user instantaneously with results from a dynamic thermal program without subjecting architects to the disadvantages of this type of program. Indeed, the program provides annual thermal needs and thermal comfort (winter and summer) in relation with orientation, building footprint, window area and type, internal gains, presence of external or internal shading devices, ventilation strategy applied and thermal mass. This design tool is based on Belgian climatic weather data but the same step could be transposed to other climatic data.
Due to the rising awareness of climate change and resulting building regulations worldwide, building designers increasingly have to consider the energy performance of their building designs. Currently, performance simulation is mostly executed after the design stage and thus not integrated into design decision-making. In order to evaluate the dependencies of performance criteria on form, material and technical systems, building performance assessment has to be seamlessly integrated into the design process. In this approach, the capability of building information models to store multi-disciplinary information is utilized to access parameters necessary for performance calculations. In addition to the calculation of energy balances, the concept of exergy is used to evaluate the quality of energy sources, resulting in a higher flexibility of measures to optimize a building design. A prototypical tool integrated into a building information modelling software is described, enabling instantaneous energy and exergy calculations and the graphical visualisation of the resulting performance indices.
The objective of this work is to implement a simplified calculation procedure for building net energy need, based on a quasi-steady state model and on a monthly data set. In particular, it is intended to supply a formulation of the dynamic parameters and to adapt them to Italian climatic, typological, constructive and user data. The method was validated by determining the numerical correlations of the gain/loss utilization factor, through a comparison with a detailed building energy simulation software (EnergyPlus). The simulation was run on some test rooms defined by CEN (European Committee for Standardization) and on some real buildings that are representative of the Italian building stock, assuming weather data from different Italian locations (Torino, Roma, Palermo). The work shows that the accuracy of results is greatly affected by nonlinearities in the determination of the heat transfer and that the dynamic parameters are sensitive to some building features which are not taken into account in the CEN correlations.
While it is possible to check the energy performance of a given building by means of several available methods, the inverse problem of determining the optimum configuration given a desired performance is more difficult to solve. In the Mediterranean region this problem is more complex due to the following two reasons: the air-conditioning load is as important as the heating load, and the energy needs depend on a high number of architectural parameters which have different, even contradictory, effects on summer and winter loads. In this paper we present an optimization algorithm that couples pseudo-random optimization techniques, the genetic algorithms (GA), with a simplified tool for building thermal evaluation (CHEOPS) for the purpose of minimizing the energy consumption of Mediterranean buildings. Since increasing the energy performance usually requires the use of special devices resulting in a high construction cost, we also propose to use GA for the purpose of economical optimization.
Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping. A brief description of each algorithm is presented along with a pseudocode to facilitate the implementation and use of such algorithms by researchers and practitioners. Benchmark comparisons among the algorithms are presented for both continuous and discrete optimization problems, in terms of processing time, convergence speed, and quality of the results. Based on this comparative analysis, the performance of EAs is discussed along with some guidelines for determining the best operators for each algorithm. The study presents sophisticated ideas in a simplified form that should be beneficial to both practitioners and researchers involved in solving optimization problems.
In Turkey, heat loss from buildings is one of the primary sources of energy waste since no or little insulation is used in existing and new buildings. Therefore, considerable energy savings can be obtained by using proper thickness of insulation in buildings. Given the significant climatic variations that exist in different parts of Turkey, 16 cities from four climate zones of Turkey are selected for analysis and optimum insulation thicknesses, energy savings, and payback periods are calculated. The annual heating requirements of buildings in different climates zones were obtained by means of the heating degree-days concept. The optimization is based on life-cycle cost analysis. Five different fuels; coal, natural gas, fuel oil, liquefied petroleum gas (LPG), and electricity, and as an insulation material polystyrene are considered. The results show that optimum insulation thicknesses vary between 2 and 17 cm, energy savings between 22% and 79%, and payback periods between 1.3 and 4.5 years depending on the city and the type of fuel.
Building simple and effective models are essential to many applications, such as building performance diagnosis and optimal control. Detailed physical models are time consuming and often not cost-effective. Black box models require large amount of training data and may not always reflect the physical behaviors. In this study, a method is proposed to simplify the building thermal model and to identify the parameters of the simplified model. For building envelopes, the model parameters can be determined using the easily available physical details based on the frequency characteristic analysis. For the building internal mass involving various components, it is very difficult to obtain the detailed physical properties. To overcome this problem, the building internal mass is represented by a thermal network of lumped thermal mass and the parameters are identified using operation data. Genetic algorithm (GA) estimators are developed to identify these parameters. The simplified dynamic building energy model is validated on a real commercial office building in different weather conditions.
Buildings are slowly replacing long-term investments that consume a lot of energy. Given current economic, as well as environmental constraints on energy resources, the energy issue plays an important role in the design and operation of buildings. Careful long-term decisions in the design and operation of buildings can significantly improve their thermal performance and thus reduce their consumption of energy. Alternative building design strategies, standards compliance and economic optimization can be evaluated using available energy analysis techniques. These range from simplified manual energy analysis methods for approximate energy use estimates to detailed computerized hourly simulation. The availability and ease of use of today’s computers make them effective tools in the decision-making process of building design. This paper reviews the most common building energy analysis techniques and the potential applications of computer technology in the energy simulation and optimization of buildings.
Computer models for the simulation of the thermal performance of buildings have been in existence for many decades. Although these programs save a great deal of time and effort in computational terms they still rely on designers intuition and experience to achieve optimum solutions for a design problem. This paper presents a computer model which, not only simulates the thermal performance of the building taking into account design variables related to the building's envelope and fabric, but also applies numerical optimization techniques to automatically determine the optimum design variables, which achieve the best thermal comfort conditions. The main optimization program is supported by a graphical model for the design of window shading devices, which uses the numerical coefficients that define the window shading to model shading devices taking into account seasonal variations in solar angles and solar gains. The rationale and methodology used to develop the models is outlined and the resulting programs are described with examples of outputs. Finally, it is concluded that the models offer a valuable decision support system for designers at an early design state for the optimization of the passive thermal performance achieving optimum thermal comfort and savings in heating and cooling energy.
En France, le secteur du bâtiment est le plus gros consommateur d’énergie parmi les secteurs économique, avec 43% de l’énergie finale totale et 25% des émissions de CO2. Il s’avère donc nécessaire de réduire l’impact environnemental de ce secteur en promouvant la construction des bâtiments neufs et la rénovation thermique des bâtiments existants, selon les critères des bâtiments à basse consommation d’énergie.
L’objectif de ce travail est de développer une méthodologie pour réaliser des études de conception de bâtiments à basse consommation d’énergie. La méthodologie consiste à déterminer des modèles polynômiaux pour l’évaluation des performances énergétique et du confort thermique d’été des bâtiments, à l’aide de la méthode des plans d’expériences et des outils de simulation numérique. Ces modèles polynômiaux permettent de simplifier les études paramétriques, en apportant une réponse alternative aux outils de simulations numériques pour la recherche de solutions afin de concevoir des bâtiments à basse consommation d’énergie. La méthodologie est appliquée sur un bâtiment tertiaire à savoir un immeuble de bureaux.
Dans le premier chapitre, nous présentons l’état de l’art des bâtiments à basse consommation d’énergie et à énergie positive, dans le but de dresser un bilan de connaissances sur le contexte énergétique français, sur les labels mis en place en France et à l’étranger, sur les projets réalisés et sur les techniques utilisées pour concevoir des bâtiments à basse consommation d’énergie.
Ensuite, nous nous focalisons dans le second chapitre, sur le développement de modèles numérique nécessaires à l’élaboration de la méthodologie. Les modèles sont développés dans l’environnement MATLAB/SIMULINK et intégrés dans la bibliothèque SIMBAD, dédiée à la simulation numérique en thermique du bâtiment afin de participer à son développement. De plus, nous présentons des études d’évaluation énergétiques de systèmes spécifiques aux bâtiments à basse consommation d’énergie qui illustrent l’utilisation des modèles numériques développés.
Un cas d’étude est défini dans le troisième chapitre ainsi que les contextes climatiques à considérer, les principes de base de la méthode des plans d’expériences et un exemple de son application. Le cas d’étude considéré est un immeuble de bureaux, nommé Beethoven, dont les caractéristiques de base seront choisies selon les exigences de la réglementation thermique. Ces caractéristiques constituent la configuration de référence qui est améliorée en suivant la méthodologie développée. L’analyse des huit zones climatiques définies par la réglementation thermique et l’évaluation des performances énergétiques du bâtiment pour la configuration de référence par rapport à ces climats, permettent de sélectionner trois climats représentatifs pour la suite du travail. Enfin, un exemple d’application de la méthode des plans d’expériences pour une optimisation énergétique de la configuration de référence permet de justifier le choix de cette méthode.
Le début du quatrième chapitre est consacré au développement des modèles polynômiaux pour l’évaluation des performances énergétique et du confort thermique d’été du bâtiment Beethoven. Nous débutons ce chapitre par une évaluation des limites de la méthode des plans d’expériences pour déterminer ces modèles polynômiaux. Il en découle une méthodologie générale d’application de la méthode des plans d’expériences afin de développer des modèles polynômiaux pour réaliser des études de conception de bâtiment à basse consommation d’énergie. Ensuite, nous effectuons, à l’aide de ces modèles, une étude de sensibilité pour le bâtiment Beethoven et une analyse de solutions pour concevoir un bâtiment à basse consommation d’énergie selon divers critères énergétiques.
Dans le dernier chapitre, nous présentons un exemple d’application des modèles polynômiaux développés pour identifier des solutions pour la conception de l’enveloppe et des systèmes du bâtiment Beethoven, afin d’obtenir un bâtiment à basse consommation d’énergie, selon les critères du label Français Effinergie et du label Allemand Passivhaus. Les configurations basse consommation d’énergie obtenues sont comparées par rapport à la configuration de référence en termes de performances énergétiques, de confort thermique d’été et d’émissions CO2.
La méthodologie que nous proposons permet d’identifier, de manière simple et rapide, des solutions pour concevoir des bâtiments à basse consommation d’énergie. Les solutions sont sélectionnées à l’aide d’abaques définis avec les modèles polynomiaux développés. Le niveau de précision constaté par rapport à la simulation numérique est appréciable. Le choix des solutions est effectué parmi des millions de configurations de facteurs, déterminées à l’aide des modèles polynômiaux. La détermination de toutes ces configurations serait difficile voire impossible à réaliser directement à l’aide de la simulation numérique, sans avoir recours à des modèles polynomiaux, d’où l’avantage d’une telle méthodologie.
Enfin, cette méthodologie constitue une base robuste pour le développement d’outils d’aide à la décision, destinés aux différents acteurs du secteur du bâtiment pour la conception des bâtiments neufs et la rénovation thermique des bâtiments existants, selon les critères des bâtiments à basse consommation d’énergie.
The global energy yearly consumption of commercial buildings in France varies between 300 and 380 kWh/m². This consumption refers to total energy consumption: lighting, heating, cooling, office automation, plug consumptions, hot water and auxiliaries. This work has defined a method to determine the best available technical solutions that lead to important energy savings in air-conditioned commercial buildings. Consumptions were reduced by a factor of 4 to 5 times. A typology of representative buildings was developed according to each sector. HVAC system types were matched to appropriate building types. Simplified models were used for HVAC systems and building management components. To solve the problem of carrying a large number of simulations due to the consideration of a lot of variables, fractional experimental designs were used. A new methodology was developed in the building sector in order to reduce the time of simulations. A parametric model expressing the different sectors of annual consumptions was developed. This model can be used without going back to simulations in order to estimate different types of energy consumptions. It can be used for a technical-economical optimization. The effect of insulation on cooling and heating consumptions was studied. Two principal sectors were considered: office buildings and hospitals. The first one is presented in details while the second is studied briefly. This work has permitted to establish a complete methodology for reducing energy consumption and to study the effect of each variable on the yearly total consumption.
Les plans d’expériences: De l’expérimentation à l’assurance qualité
- M.-C S Gilles Sado
M.-C.S. Gilles Sado, Les plans d'expé riences: De l'expé rimentation a ` l'assurance qualité, Paris, 2000.
Dé veloppement d'une mé thodologie de conception des bâ a ` basse consommation d'e ´ nergie
- F Chlela
F. Chlela, Dé veloppement d'une mé thodologie de conception des bâ a ` basse consommation d'e ´ nergie, Ph.D. Thesis, Université de la Rochelle, 2008.
The MINERGIE -Standard for Buildings
MINERGIE, The MINERGIE -Standard for Buildings, Bern, 2008.
17
Les plans d’expériences par la méthode de Taguchi, Editions d’Organisation
- M Pillet