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Green Design Studio: A modular-based approach for high-performance building design
Abstract
A modular-based Green Design Studio (GDS) platform has been developed in this study for fast and accurate performance analysis for early stage green building design. The GDS platform aims to simplify the design and analysis process by embedding performance parameters into design elements in modules and employing near-real-time model for whole building performance simulation as well as by providing an easy-to-use and intuitive user interface to assist users without extensive knowledge on building physics. The platform consists of building modules as fundamental building blocks, performance predicting models, and a user interface for visualization and interactive design. In the platform, a whole building is composed of modules organized in a hierarchical structure, including spaces, enclosures, service systems, sustainable resource systems and sites. Both physics-based and data-driven models can be used to simulate the building performance and optimize building systems. A simplified physics-based model, the Resistance–Capacitance (RC) model, has been proposed as a generic simulation model for the flows of heat, air, moisture and pollutants, which is significantly faster than conventional simulation tools such as EnergyPlus, and hence more practical for use in real-time design interaction and optimization. A pilot case study is conducted to illustrate the modular-based design approach using a section of an office building. Compared to conventional building performance analysis tools, the GDS platform can provide fast and reliable feedback on performance prediction for early design. The modular approach makes it easier to modify the building design and evaluate the potentials and contributions of various green design features and technologies.
... The built environment and construction sector are widely recognized for their Buildings 2024, 14, 2020 3 of 17 significant environmental impacts [9], with buildings consuming about 40% electricity, 30% water, and producing substantial waste. A comprehensive approach to high-performance building design is essential to improve energy efficiency, indoor environmental quality, water efficiency, material use reduction, and site sustainability [10]. Over the past two decades, there has been notable growth in designing high-performance buildings, also referred to as green or sustainable building design [11,12]. ...
... To enhance environmental building performance, a broader understanding of sustainability is required, as traditional design processes may not be environmentally friendly. High-performance building design typically involves several stages, including early design, preliminary design, design development, detailed design, and construction documentation [10]. During early design (or conceptual design), architects and clients establish initial design strategies without detailed building performance analysis by system engineers. ...
... Decisions made in the conceptual design stage significantly impact the final building performance. Integrated building design integrates design and performance analysis across various stages, including conceptual design, using Building Information Modeling (BIM) throughout the building's lifecycle, from design through construction to operation [10]. ...
Buildings exert a profound influence on the environment, with the design phase recognized as the pivotal determinant of a building’s overall performance. Green building design, in particular, introduces heightened complexity, where the attributes of the design team play a pivotal role in shaping performance outcomes. Consequently, the characteristics of the design team emerge as crucial factors in the enhancement of both green building design performance and client attributes. This study aims to empirically examine a model formulated to gauge the extent to which Effective Design Team Attributes contribute to the enhancement of performance in designing green buildings and influencing client attributes. To achieve this objective, a comprehensive questionnaire survey was administered to professionals within the architecture and engineering domains actively engaged in the design and consulting sectors of the building industry. The collected data underwent meticulous scrutiny for authenticity and dependability using the WINSTEPS 5.2.5 software before undergoing subsequent analysis. Statistical analyses were conducted using SPSS version 19, with Principal Components Analysis (PCA) and the Structural Equation Modeling (SEM) approach implemented through Amos version 18 to derive the most robust model. The findings underscore the pivotal role of an adeptly managed design team in significantly improving both the performance of green building designs and the qualities of clients. Rasch’s analysis confirmed the validity of our 5-point Likert scale for Design Green Building Performance (DGBP), Effective Design Team Attributes (EDTA), and Client Qualities (CQ). All items demonstrated excellent reliability, separation, and discrimination, ensuring robust data quality. Dimensionality tests revealed the appropriateness of response categories, indicating satisfactory scale performance. The Effective Design Team Model, validated through Principal Components Analysis (PCA), exhibited a satisfactory fit, supported by significant chi-square statistics, high goodness-of-fit indices, and acceptable root mean square residual values. Client attributes displayed a strong association with effective design team management, validating key model elements. The intricacies inherent in the design process can be mitigated by adopting the green design charrette approach. Consequently, the establishment of an effective design team, coupled with green design leadership, active participation, and clarity in roles and responsibilities, emerges as a potent strategy for elevating the performance level of green building designs.
... The only exceptions found respond to a purely graphic exploration of data [49] and to a presentation focused on the BIM-GIS integration systems where only the display mechanisms are explained [50]. By contrast, only 33% of tools have been validated with their target user; this is a problem that many authors indicated as a limitation and a future research topic [2,10,44,46,50,52,63,64]. 4. ...
... Among the charts aimed at this purpose, in addition to the typical lines and bars, histograms, scatter plots, parallel coordinates, as well as pie charts and their variations were found. In most cases, these graphs complement data tables that present the same numerical information [1,10,51,57,58,63,64], giving the expert user the opportunity to interpret data under different possible relationships between variables. ...
... It offers the possibility to identify patterns [78] or separate clusters [71] in search of anomalies and allows for the analysis of design performance according to different alternatives [54]. Histograms have proven useful when comparing hourly and daily consumption [55,62,64], as well as weekly and monthly variations [44,47,60,61]. Historical performance and design variables can also be plotted using this graph [38,54]. 5. Water and natural gas consumption. ...
Data visualization has become relevant in the framework of the evolution of big data analysis. Being able to understand data collected in a dynamic, interactive, and personalized way allows for better decisions to be made when optimizing and improving performance. Although its importance is known, there is a gap in the research regarding its design, choice criteria, and uses in the field of building energy consumption. Therefore, this review discusses the state-of-the-art of visualization techniques used in the field of energy performance, in particular by considering two types of building analysis: simulation and monitoring. Likewise, data visualizations are categorized according to goals, level of detail and target users. Visualization tools published in the scientific literature, as well as those currently used in the IoT platforms and visualization software, were analyzed. This overview can be used as a starting point when choosing the most efficient data visualization for a specific type of building energy analysis.
... However, the scientific literature reveals a critical gap: few studies integrate physics-based building energy models (BEMs) into real-world guest allocation strategies. Most related works either focus on surface-level photovoltaic generation [8,9] or on aggregated HVAC performance simulations, without addressing room-level thermal behavior influenced by geometry, orientation, and floor height [10,11]. ...
... Building energy models (BEMs) have become essential tools in building performance analysis. They enable thermal simulations under different design and operational conditions, accounting for architectural features, materials, orientation, and usage schedules [10]. Recent advancements in geometric modeling and the incorporation of urban context-such as surrounding buildings, solar obstruction, and surface reflectivity-have increased the reliability of such simulations in dense environments [5,11]. ...
The current climate and energy crises demand innovative approaches to operating buildings more sustainably. HVAC systems, which significantly contribute to a building’s energy consumption, have been a major focus of research aimed at improving operational efficiency. However, a critical factor often overlooked is the seasonal and hourly variation in solar radiation and the resulting solar heat gain, which heats specific rooms differently depending on their orientation, type, and location within the building. This study proposes a simulation-based strategy to reduce HVAC energy use in hotels by allocating guests to rooms with more favorable thermal characteristics depending on the season. A high-resolution building energy model (BEM) was developed to represent a real 17-floor hotel tower in Madrid, incorporating detailed geometry and surrounding shading context. The model includes 439 internal thermal zones and simulates solar radiation using EnergyPlus’ Radiance module. The simulation results revealed large room-by-room differences in thermal energy demand. When applying an energetically optimized guest allocation strategy based on these simulations and using real occupancy data, potential reductions in HVAC energy demand were estimated to reach around 6% during summer and up to 20% in winter. These findings demonstrate that data-driven guest allocation, informed by physics-based building simulations, can provide substantial energy savings without requiring physical renovations or equipment upgrades, offering a promising approach for more sustainable hotel operation.
... The ongoing and futile task of building connections to make the interactive design and analysis process seamless and effective in Virtual Reality/Augmented Reality (VR/ AR) remains unresolved [14]. Comfort conditions in the building can be maintained using the heat pump and the heat accumulator. ...
... In green buildings, green is associated with ecology, in which high-performance energy conservation techniques are considered [14]. Some important factors that are necessary for the designing of the green building are ecologically identical without much altering from existing by using clean materials, natural water resources, ecological conversion, maximum utilisation of renewable resources, and rainwater harvesting system [15]. ...
The core issue these days is to reduce carbon dioxide (CO2) emission and prevent the hole in the ozone layer. Numerous emerging environmental crises successfully tackled by coordinated global efforts in the past few years. Despite this, the present climate emergency is a much more serious threat than anything we have ever encountered and requires much more action consequently. Today, more attention is paid on green buildings in the society, which will make use of the sustainable and energy-efficient buildings a necessity for future generations. Hence, this paper proposes a novel design model of an energy-efficient residential green building with low carbon emission to maintain the health and enhances the productivity and living standards of inhabitants. Green building technology is utilised to enhance energy efficiency and lower carbon emission. This design considers green, recyclable, and eco-friendly building materials, which are beneficial for human health and comply with relevant Indian standards and building codes. This building design proposes Renewable Energy Sources (RES) integrated with the power grid, although RES powers most of the load of the proposed green building. The suggested green building design shows effective results, i.e., building energy consumption has reduced by 50.54%, total energy consumption cost has reduced by 57.41%, and CO2 emission per month has reduced by 50.54%. In addition, stormwater-harvesting system is proposed to collect 54,322.23 L of rainwater annually, which helps in water conservation and contributes to improve the groundwater level. The proposed solid waste management plan has contributed to the achievement of regional and national Sustainable Development Goals (SDGs). Finally, there are some suggestions to promote the use of green buildings for sustainable development.
... The building sector is a major consumer of energy and an emitter of greenhouse gases, accounting for 40% of global resource use, 30% of total energy consumption, and 30% of global carbon emissions [1][2][3][4][5][6]. With nearly half of the world's population residing in urban areas [7], human activities like deforestation, land-use changes, urbanization, and construction development have made the building sector a critical concern for contemporary societies [8]. ...
... The most recognized systems, forming the basis for many others worldwide, are the Building Research Establishment Environmental Assessment Method (BREEAM), Leadership in Energy and Environmental Design (LEED), and the Sustainable Building Tool (SBTool), established by the International Initiative for a Sustainable Built Environment (iiSBE) [16]. Additionally, various individual studies have explored sustainable building design indicators and their evaluation through Multi-Criteria Decision Making (MCDM) analysis [1,[17][18][19][20][21]. ...
The proliferation of sustainable design approaches and assessment methods has resulted in a vast array of indicators. However, this abundance often leads to confusion during interpretation and application. Additionally, rapid urbanization and environmental concerns sometimes overshadow social and economic considerations, emphasizing environmental impact reduction. This study addresses these challenges through an integrated approach that combines a Systematic Literature Review (SLR) with a Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis to provide a holistic model for sustainable building design. The SLR was carried out individually through a relative Structural Query Language (SQL) regarding sustainable building design and vernacular principle. The output of SLR was subjected to DEMATEL model to recognize the holistic indicators interconnection and validate the proposed model. The research identified 23 global indicators for building sustainability worldwide, with five—Energy, Materials & Resources, Sites & Ecology, Indoor Environmental Quality, and Water—emerging as the most prevalent. Additionally, 22 consistently applied indicators in vernacular design practices exhibited significant overlap with those in sustainable design. This model integrated two novel indicators—Vernacular Principles and Social—Culture—with common sustainable building indicators. These primary indicators complement the common and applicable sustainable building indicators, ensuring a balanced approach that considers global contexts. DEMATEL analysis confirmed the validity and interconnection of these indicators, emphasizing the critical role of vernacular principles in achieving true sustainability.
... To construct an optimization model using physical simulation methods, simulation tools are needed to compute building-performance metrics, such as EC calculations based on heat transfer or thermodynamic theories [89]. BPS tools are being made to be more userfriendly by equipping the simulation engines with certain kinds of graphical user interfaces (GUIs) [90]. An analysis of the BPS tools used in the reviewed papers ( [95]) are the primary tools used in simulation research. ...
... An optimization analysis necessitates numerous evaluations to achieve near-optimal solutions, leading to relatively extended processing times. Hence, the performance of optimization algorithms plays a crucial role in the efficiency of the optimization process, prompting the selection of suitable methods tailored to specific research requirements [90]. ...
In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. Data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account; from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6.7% of the literature focus on the impacts of human behavior and climate change on building retrofits, respectively. In the future, it is necessary to give further consideration to user behaviors and long-term climate change in the retrofit process, in addition to improving the accuracy of optimization models and exploring the generalization and migration capabilities of surrogate models.
... Furthermore, Tsoka et al. [46] compared the energy performance of the conventional and prefabricated building and proved that the later one showed significant advantages. Currently, some researchers started paying attention to the green design that integrated the digital technology of modular buildings to achieve sustainability at the early design stage and contribute to the whole building cycle [47,48]. The early green design also benefited future modules' reuse [49], which was one of the most important strengths of prefabricated construction. ...
... However, research on water footprints is still scarce. Besides, although a few researchers have begun discussing and proposing strategies for prefabricated green design [48], recycling [156], and reuse [49], the reusable issue that seriously affects environmental and economic benefits still needs further exploration. Moreover, prefabricated buildings of different structural types may have different performances. ...
As a game-changing technology with significant environmental, economic, and social benefits, prefabricated technology has attracted attention and has been increasingly adopted in the construction industry. Although multitudinous studies have investigated various aspects of prefabrication in construction, a thorough review of its current development state that synthesized environmental, economic, and social sustainability dimensions remains overdue. Therefore, this study aims to fill this research gap by constructing a systematic framework, analyzing the research status quos, and providing recommendations for future research. This study first conducted a holistic review of 768 references with NVivo. A research foci framework that represented the body of knowledge in prefabrication in construction was developed with five levels, which were advantages, hindrances, stakeholders, promotion policies, and strategy spectrum. Following the framework, the in-depth analyses from the perspectives of environmental, economic, social sustainability, technologies development, and promotion strategies were performed. The current research domains were further linked with potential research directions for promoting prefabricated construction towards sustainability. The study is of value in both offering references for policy formulation and stakeholder practice and providing recommendations for future research.
... With nearly half of the global population residing in urban areas [3], the building sector emerges as a critical contributor to resource consumption, energy use, and greenhouse gas (GHG) emissions [4]. Studies indicate that buildings account for approximately 40% of resource utilization, 30% of total energy consumption, and 30% of global carbon emissions [5][6][7][8][9][10]. Air pollution represents a pressing challenge, particularly in Kabul, the capital of Afghanistan [11]. ...
This study investigates the potential of Tawa Khana, a vernacular heating system used in Asian countries, particularly Afghanistan, to improve building energy efficiency. Tawa Khana utilizes radiant floor heating, drawing warmth from a cooking stove or fireplace to heat the floor and subsequently the room. However, it is neglected in contemporary designs, and there is a predicted risk of losing this valuable knowledge entirely within the next few decades. This study aims to evaluate Tawa Khana's features, including its design and construction methods, with the goal of establishing a design manual for Tawa Khana. Furthermore, it analyzes the multifaceted contributions of Tawa Khana to building energy efficiency. For this purpose, a case study evaluation with comprehensive simulations analysis through the DesignBuilder program was conducted. The results indicate that Tawa Khana is applicable with minimal additional technology, increasing room temperature by 4 degrees Celsius, demonstrating its effectiveness as a sustainable heating strategy. This vernacular technique efficiently utilizes waste heat from cooking activities, reducing reliance on conventional heating systems and promoting energy conservation.
... Given these factors, sustainable architecture has emerged as a critical trend in the 21st century, driven by environmental concerns and the need for more sustainable building technologies and practices [15,16]. Numerous design strategies, standards, and assessment methods have been developed to promote sustainable building design at various scales [5,[17][18][19]. However, a gap persists between theoretical approaches and practical implementation. ...
The building sector is a major contributor to resource consumption, energy use, and greenhouse gas emissions. Sustainable architecture offers a solution, leveraging Building Energy Modeling (BEM) for early-stage design optimization. This study explores the use of genetic algorithms for optimizing sustainable design strategies holistically. A comprehensive analysis and optimization model was developed using genetic algorithms to individually optimize various sustainable strategies. The optimized strategies were then applied to a pre-existing building in Kabul City, a region facing significant environmental challenges. To enhance accuracy, this study integrated energy simulations with Computational Fluid Dynamics (CFD). This research combines genetic algorithms with energy simulation and CFD analysis to optimize building design for a specific climate. Furthermore, it validates the optimized strategies through a real-world case study building. Optimizing the Window-to-Wall Ratio (WWR) and shading devices based on solar exposure significantly improved the building’s energy performance. South (S)-facing single windows and specific combinations of opposing and adjacent windows emerged as optimal configurations. The strategic optimization of building component materials led to substantial energy savings: a 58.6% reduction in window energy loss, 78.3% in wall loss, and 69.5% in roof loss. Additionally, the optimized pre-existing building achieved a 48.1% reduction in cooling demand, a 97.5% reduction in heating demand, and an overall energy reduction of 84.4%. Improved natural ventilation and controlled solar gain led to a 72.2% reduction in peak-month CO2 emissions. While this study focused on applicable passive design strategies, the integration of advanced technologies like Phase Change Materials (PCMs), kinetic shading devices, and renewable energy systems can further improve building performance and contribute to achieving net-zero buildings.
... The building sector plays a critical role in sustainability discussions due to its significant resource consumption and greenhouse gas (GHG) emissions. Accounting for 40% of global resource usage, 30% of total energy consumption, and 30% of carbon emissions [1][2][3][4][5][6], buildings present a crucial area for implementing sustainable practices. Therefore, a holistic approach to sustainable development is imperative in building design. ...
The growing emphasis on sustainable architecture, addressing environmental, social, and economic concerns, has spurred the development of numerous design strategies and assessment methods. This has resulted in many sustainable building design indicators, posing challenges in their selection and application, particularly in developing countries with limited resources. This study aims to address these gaps by employing a Systematic Literature Review (SLR) to identify all commonly used sustainable building design indicators globally. Subsequently, a comparative analysis of the SWARA and AHP methods was conducted to characterize weighting scores, prioritize and adapt evaluated indicators, and identify suitable methods for their selection and analysis. Furthermore, the study proposed a comprehensive and holistic set of indicators for sustainable building design, targeting architects and policymakers. Within this set of indicators, the study identified five as the most globally applicable and critical for achieving building sustainability. Weighting scores and prioritization of these indicators for Kabul City, largely aligned with common rating system indicators, were as follows: Energy Efficiency (27.92% weighting), Material & Resources (19.57% weighting), Site & Ecology (13.92% weighting), Indoor Environment Quality (7.69% weighting), and Water Efficiency (13.87% weighting). The overall results indicated that both methods AHP and SWARA are highly effective for analyzing and adapting indicators for sustainable design. These findings offer valuable insights and guidance for the analysis of sustainable indicators, fostering the development of holistic design approaches and a rating system. Ultimately, this research contributes to a more sustainable built environment, particularly within the context of Kabul city.
... Improvement of building energy efficiency is increasingly needed due to climate change concerns [1]. Evaluation and optimization of a building's energy efficiency and overall performance can be achieved using building performance simulation software (e.g., EnergyPlus, DOE2, TRNSYS) and computer programs [2,3]. Usually, information regarding construction, occupancy patterns, heating, ventilation, and air conditioning (HVAC), and boundary conditions such as climate information is included in building performance simulations [4]. ...
This study aims to demonstrate the comprehensive development of typical meteorological years (TMYs) under relatively limited observational data. The distribution of missing hourly observational data of the 2011–2020 period at all sites was examined. This paper proposes a quality control method for filling the gaps in the missing hourly observational data using bias-corrected ERA5 reanalysis data in the process of developing TMYs. Initially, the temperature bias distribution from −4.5 °C to 2.7 °C was reduced to a range of −0.014 °C to 0.005 °C. The relative humidity bias distribution was −6 % to 10 %, and was reduced to −0.32 % to 0.07 %. The bias distribution of wind speeds ranging from −4 m/s to 2 m/s was reduced to −0.02 m/s to 0.35 m/s. The Sandia method with a modified weighting of Finkelstein-Shaffer (FS) statistics was applied to eight climate elements, namely, global horizontal irradiance, direct normal irradiance, diffuse horizontal irradiance, temperature, precipitation, wind speed, relative humidity, and dew point temperature to generate TMYs at 106 sites across eight climate zones in Indonesia. The verification results showed that the average correlation and RMSE between TMYs and their long-term averages were 0.96 and 75 w/m2 for global horizontal radiation, respectively, while those for temperature were 0.86 and 1.3 °C, respectively.
... The detailed physical method is based on analytical relationships among various building components (e.g., envelope, HVAC system, plants, terminal equipment) through physics theories and numerous formulas. With the development of programming technology, simulation programs (e.g., EnergyPlus, DOE2, TRNSYS) embedded with these physical models have been developed rapidly into visualization tools with graphical user interfaces (GUI) [22,26]. The statistical and regression method focuses on correlations between condition parametric setting and system structure, and historical energy data. ...
As one of the most important and advanced technology for carbon-mitigation in the buildings sector, building performance simulation (BPS) has played an increasingly important role with the powerful support of building energy modelling (BEM) technology for energy-efficient designs, operations, and retrofitting of buildings. Owing to its deep integration of multidisciplinary approaches, the researchers, as well as tool developers and practitioners, are facing opportunities and challenges during the application of BEM at multiple scales and stages, e.g., building/system/community levels and planning/design/operation stages. By reviewing recent studies, this paper aims to provide a clear picture of how BEM performs in solving different research questions on varied scales of building phase and spatial resolution, with a focus on the objectives and frameworks, modelling methods and tools, applicability and transferability. To guide future applications of BEM for performance-driven building energy management, we classified the current research trends and future research opportunities into five topics that span through different stages and levels: (1) Simulation for performance-driven design for new building and retrofit design, (2) Model-based operational performance optimization, (3) Integrated simulation using data measurements for digital twin, (4) Building simulation supporting urban energy planning, and (5) Modelling of building-to-grid interaction for demand response. Additionally, future research recommendations are discussed, covering potential applications of BEM through integration with occupancy and behaviour modelling, integration with machine learning, quantification of model uncertainties, and linking to building monitoring systems.
... All papers went through the rigorous peer review process of the journal. A brief overview of each paper is given below: The paper by Shen et al. (2021) developed a new modularbased Green Design Studio (GDS) platform for fast and accurate performance analysis for early stage green building design. A simplified physics-based model, the Resistance-Capacitance (RC) model was proposed as a generic simulation model, which is significantly faster than conventional simulation tools such as EnergyPlus, and hence more practical for use in real-time design interaction and optimization. ...
Healthy building design is an emerging field of architecture and building engineering. Indoor air quality (IAQ) is an inevitable factor that should be considered in healthy building design due to its demonstrated links with human health and well-being. This paper proposes to integrate IAQ prediction into healthy building design by developing a simulation toolbox, termed i-IAQ, using MATLAB App Designer. Within the i-IAQ, users can input information of building layout and wall-openings and select air pollutant sources from the database. As an output, the toolbox simulates indoor levels of carbon dioxide (CO2), total volatile organic compounds (TVOC), inhalable particles (PM10), fine particles (PM2.5), nitrogen dioxide (NO2), and ozone (O3) during the occupied periods. Based on the simulation results, the toolbox also offers diagnosis and recommendations to improve the design. The accuracy of the toolbox was validated by a case study in an apartment where physical measurements of air pollutants took place. The results suggest that designers can integrate the i-IAQ toolbox in building design, so that the potential IAQ issues can be resolved at the early design stage at a low cost. The paper outcomes have the potential to pave a way towards more holistic healthy building design, and novel and cost-effective IAQ management.
Building energy modeling, also known as building energy simulation, has developed rapidly in recent years and plays a crucial role in building life-cycle analysis. It can be employed in the design phase to predict the energy consumption of different design schemes and evaluate various control and retrofitting measures at the operation stage. In such simulations, it is commonly understood and accepted that the simulated relative differences are more reliable than the predictions of absolute energy results. However, whether this common understanding is true is yet to be thoroughly investigated. In this study, we investigate the simulated relative differences and the extent to which they are affected by the degree of model input deviation. Simulation and Monte Carlo approaches are adopted for the analysis. The results indicate that the simulated relative differences are not as reliable as expected, and the outputs strongly depend on the degree of the model input deviation. When the degree of deviation is less than 15% or the model inputs are within reasonable ranges, the simulated relative differences match the baseline obtained using Monte Carlo simulations. Moreover, the model’s error indicators meet the requirements of the ASHRAE Guideline 14–2014 when the degree of input deviation is below 15%.
Buildings consume approximately 39% of the total energy used in US, of which 53% is consumed by residential buildings. Besides, indoor air quality (IAQ) have significant impacts on occupant health since people spend on average around 90% of their time indoors. Nowadays, a great number of green building technologies (GBTs) have been developed and implemented in buildings for reducing energy consumption and improving IAQ. This paper proposes an approach to develop a green building technology database for residential buildings including their the technology’s feature and performance for energy saving and IAQ improvement under different building configuration and climate conditions. The GBTs are collected from case study buildings. For each study case, the GBTs are classified by the Virtual Design Studio (VDS) building assessment method. A local reference building is first defined for the region where the case building is constructed. Both forward-step evaluation of a proposed GBT to a reference building and backward-step tracking of the contribution of the technology to the case building are conducted. A scalability analysis is also conducted to understand the practical application of the performance parameters to other cases with different building design. EnergyPlus and CHAMPS-Multizone are used to analyse the energy and IAQ performance for each technology. The approach is verified by a case study of two single-family houses in US.
Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control. Black box building energy modeling approach has been heavily studied in past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and ASHRAE clear sky solar radiation model. Although building energy model involves two important parts of building component - envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.
Despite its marked success in recent years, it is still not clear how Virtual Reality (VR) can assist architects at the early stages of ideation and design. In this paper, we approach VR to build and explore maquettes at different scales in early design stages. To this end we developed a VR environment where user interactions are supported by untethered, easy to operate, peripherals, using a mobile virtual reality headset to provide virtual immersion and simplified geometric information to create voxel-based maquettes. Usability studies with laypeople suggest that the proposed system is both easier to use and more effective [better suited] than current CAD software to rapidly create simplified models. Additionally, tests with architects have shown the system's potential to improve their toolset. This is partly due to VR combining real-time performance with immersive exploration of the content, where body-scale relationships become visible to support the creative process, allowing architects to become both builders and explores of spatial constructs.
This paper deals with the problem of cost-optimal operation of smart buildings that integrate a centralized HVAC system, photovoltaic generation and both thermal and electrical storage devices. Building participation in a Demand-Response program is also considered. The proposed solution is based on a specialized Model Predictive Control strategy to optimally manage the HVAC system and the storage devices under thermal comfort and technological constraints. The related optimization problems turn out to be computationally appealing, even for large-scale problem instances. Performance evaluation, also in the presence of uncertainties and disturbances, is carried out using a realistic simulation framework.
Different researchers found an influence of the air temperature, the air humidity and the air velocity on the perceived air quality, which within the olf-decipol method of Fanger is not taken into account. It is possible, with the aid of the freshness of the air and the olf-decipol method of Fanger, to distract a methodology for the evaluation of the perceived air quality depending on the air temperature, the air humidity and the air pollution, caused by human bioeffluents. The aim of this study is to incorporate air velocity at neck height, as an extra parameter in this methodology, as well.
Importance
Exposure to ozone has been associated with cardiovascular mortality, but the underlying biological mechanisms are not yet understood.
Objective
To examine the association between ozone exposure and cardiopulmonary pathophysiologic mechanisms.
Design, Setting, and Participants
A longitudinal study involving 89 healthy adult participants living on a work campus in Changsha City, China, was conducted from December 1, 2014, to January 31, 2015. This unique quasiexperimental setting allowed for better characterization of air pollutant exposure effects because the participants spent most of their time in controlled indoor environments. Concentrations of indoor and outdoor ozone, along with the copollutants particulate matter, nitrogen dioxide, and sulfur dioxide, were monitored throughout the study period and then combined with time-activity information and filtration conditions of each residence and office to estimate 24-hour and 2-week combined indoor and outdoor mean exposure concentrations. Associations between each exposure measure and outcome measure were analyzed using single-pollutant and 2-pollutant linear mixed models controlling for ambient temperature, secondhand smoke exposure, and personal-level time-varying covariates.
Main Outcomes and Measures
Biomarkers indicative of inflammation and oxidative stress, arterial stiffness, blood pressure, thrombotic factors, and spirometry were measured at 4 sessions.
Results
Of the 89 participants, 25 (28%) were women and the mean (SD) age was 31.5 (7.6) years. The 24-hour ozone exposure concentrations ranged from 1.4 to 19.4 parts per billion (ppb), corresponding to outdoor concentrations ranging from 4.3 to 47.9 ppb. Within this range, in models controlling for a second copollutant and other potential confounders, a 10-ppb increase in 24-hour ozone was associated with mean increases of 36.3% (95% CI, 29.9%-43.0%) in the level of platelet activation marker soluble P-selectin, 2.8% (95% CI, 0.6%-5.1%) in diastolic blood pressure, 18.1% (95% CI, 4.5%-33.5%) in pulmonary inflammation markers fractional exhaled nitric oxide, and 31.0% (95% CI, 0.2%-71.1%) in exhaled breath condensate nitrite and nitrate as well as a −9.5% (95% CI, −17.7% to −1.4%) decrease in arterial stiffness marker augmentation index. A 10-ppb increase in 2-week ozone was associated with increases of 61.1% (95% CI, 37.8%-88.2%) in soluble P-selectin level and 126.2% (95% CI, 12.1%-356.2%) in exhaled breath condensate nitrite and nitrate level. Other measured biomarkers, including spirometry, showed no significant associations with either 24-hour ozone or 2-week ozone exposures.
Conclusions and Relevance
Short-term ozone exposure at levels not associated with lung function changes was associated with platelet activation and blood pressure increases, suggesting a possible mechanism by which ozone may affect cardiovascular health.
This paper presents a co-simulation method using EnergyPlus and CHAMPS-Multizone for combined energy efficiency and indoor air quality (IAQ) analysis. The energy simulation estimates the energy consumption of HVAC systems, lights, and electrical equipment. The Indoor Environmental Quality (IEQ) simulation predicts the indoor contaminate concentrations for IAQ, the zone air temperature and relative humidity for controlled zone conditions, the Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) for thermal comfort, and the illumination for daylighting performance. The energy, thermal comfort, and daylighting simulation are performed by EnergyPlus while the CHAMPS-Multizone is used for the IAQ simulation. In each time step, the CHAMPS-Multizone simulates the pollutant balance, and the ventilation rate required to control the contaminant concentrations under pre-established threshold values. EnergyPlus uses the ventilation rate to simulate the heat, air, and moisture balance, and the energy and daylighting performance. The Building Controls Virtual Test Bed is used for the run-time data exchange. Simulation cases using a 3-zone building are used to validate and demonstrate the benefit of the method. The co-simulation model is developed and implemented in a Virtual Design Studio (VDS) software framework with graphical user interface for integrated building system design. The co-simulation method is found to be especially useful in analyzing the interaction between IAQ and energy efficiency measures.
The “Virtual Design Studio (VDS)” is a software platform for integrated, coordinated and optimized design of building energy and environmental systems. It is intended to assist management, architectural and systems design teams throughout the early to detailed building design stages as analyzed in Part 1 (DOI: 10.1007/s12273-013-0110-2). This paper presents an overview of the VDS design and method of software implementation, including system composition, architecture, graphical user interface (GUI), and simulation solver integration. A VDS user workflow is also illustrated with a simplified design example.
There are many sophisticated building simulators capable of accurately modelling the thermal performance of buildings. Lumped Parameter Models (LPMs) are an alternative which, due to their shorter computational time, can be used where many runs are needed, for example when completing computer-based optimisation. In this paper, a new, more accurate, analytic method is presented for creating the parameters of a second order LPM, consisting of three resistors and two capacitors, that can be used to represent multi-layered constructions. The method to create this LPM is more intuitive than the alternatives in the literature and has been named the Dominant Layer Model. This new method does not require complex numerical operations, but is obtained using a simple analysis of the relative influence of the different layers within a construction on its overall dynamic behaviour. The method has been used to compare the dynamic response of four different typical constructions of varying thickness and materials as well as two more complex constructions as a proof of concept. When compared with a model that truthfully represents all layers in the construction, the new method is largely accurate and outperforms the only other model in the literature obtained with an analytical method.
A computer simulation tool, named “CHAMPS-Multizone,” is introduced in this article for analyzing both energy and indoor air quality (IAQ) performance of buildings. The simulation model accounts for the dynamic effects of outdoor climate conditions (solar radiation, wind speed and direction, and contaminant concentrations), building materials and envelope system design, multi-zone air and contaminant flows in buildings, internal heat and pollutant sources, and operation of the building HVAC systems on the building performance. It enables combined analysis of building energy efficiency and indoor air quality. The model also has the ability to input building geometry data and HVAC system operation related information from software, such as SketchUp and DesignBuilder via IDF file format. A “bridge” to access static and dynamic building data stored in a “Virtual Building” database is also developed, allowing convenient input of initial and boundary conditions for the simulation and for comparisons between the predicted and measured results. This article summarizes the mathematical models, adopted assumptions, methods of implementation, and verification and validation results. The needs and challenges for further development are also discussed.
For the purpose of modelling indoor particle dispersion with an Eulerian drift flux model or analyzing indoor particle deposition onto various surfaces accurately, it may take considerable time to calculate the deposition velocity for each surface as numerical integration or calculation is usually needed. In this paper, a modified three-layer model is presented to calculate indoor particle deposition velocities for surfaces with different inclinations and for different friction velocities. Then, 1020 cases, covering the common indoor scenarios, were modelled to obtain a database of indoor particle deposition velocities. Based on the results of the 1020 cases, an empirical equation was generated to determine indoor particle deposition velocities. The empirical equation was divided into four parts, named the Fine zone, Coarse zone, Zero zone, and Transition zone. In the Fine zone, the friction velocity decides the particle deposition velocity, while in the Coarse zone, the inclination angle of the surface is the decisive parameter for the deposition velocity. The results show that the average error of the empirical equation to the database was 1.53%, 1.50% and 21.93% in the Fine zone, Coarse zone and Transition zone, respectively. The deposition velocities in the Zero zone can all be deemed as zero. Empirical equation predictions agree well with experimental data for a spherical chamber (Cheng, 1997). The empirical equation generated in this study is therefore applicable for easily calculating the boundary conditions for Eulerian drift flux model or analyzing indoor particle deposition onto smooth surfaces with varying inclinations with reasonable accuracy.
The Integrated Design Process (IDP) is a method of intervention in early stages of the design process that supports the development and design team to avoid sub-optimal design solutions. IDP is not a new concept, and may in fact have been applied in the past by some design teams on an ad-hoc basis; but the formal implementation of the process is a development that has taken place over the past 15 years. This paper provides a partial history and some analysis of the characteristics of IDP. The C-2000 Program One source of specific information on a formal implementation of IDP is the experience gained from a small Canadian demonstration program for high-performance buildings, the C2000 program, which was developed and managed by Natural Resources Canada (NRCan), with the author as developer of the requirements and manager of the process. The C-2000 program was designed in 1993 and launched in 1994 and was formally operational until 1999, although some projects were not completed until recently 1
Because human activities impact the timing, location, and degree of pollutant exposure, they play a key role in explaining exposure variation. This fact has motivated the collection of activity pattern data for their specific use in exposure assessments. The largest of these recent efforts is the National Human Activity Pattern Survey (NHAPS), a 2-year probability-based telephone survey (
n=9386) of exposure-related human activities in the United States (U.S.) sponsored by the U.S. Environmental Protection Agency (EPA). The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales, particularly for use in probabilistic population exposure models. NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Maryland's Survey Research Center using a computer-assisted telephone interview instrument (CATI) to collect 24-h retrospective diaries and answers to a number of personal and exposure-related questions from each respondent. The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day (i.e., each microenvironment). Between 340 and 1713 respondents of all ages were interviewed in each of the 10 EPA regions across the 48 contiguous states. Interviews were completed in 63% of the households contacted. NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles. These proportions are fairly constant across the various regions of the U.S. and Canada and for the California population between the late 1980s, when the California Air Resources Board (CARB) sponsored a state-wide activity pattern study, and the mid-1990s, when NHAPS was conducted. However, the number of people exposed to environmental tobacco smoke (ETS) in California seems to have decreased over the same time period, where exposure is determined by the reported time spent with a smoker. In both California and the entire nation, the most time spent exposed to ETS was reported to take place in residential locations.
Personal exposure to environmental substances is largely determined by time-microenvironment-activity patterns while moving across locations or microenvironments. Therefore, time-microenvironment-activity data are particularly useful in modeling exposure. We investigated determinants of workday time-microenvironment-activity patterns of the adult urban population in seven European cities. The EXPOLIS study assessed workday time-microenvironment-activity patterns among a total of 1427 subjects (age 19-60 years) in Helsinki (Finland), Athens (Greece), Basel (Switzerland), Grenoble (France), Milan (Italy), Prague (Czech Republic), and Oxford (UK). Subjects completed time-microenvironment-activity diaries during two working days. We present time spent indoors--at home, at work, and elsewhere, and time exposed to tobacco smoke indoors for all cities. The contribution of sociodemographic factors has been assessed using regression models. More than 90% of the variance in indoor time-microenvironment-activity patterns originated from differences between and within subjects rather than between cities. The most common factors that were associated with indoor time-microenvironment-activity patterns, with similar contributions in all cities, were the specific work status, employment status, whether the participants were living alone, and whether the participants had children at home. Gender and season were associated with indoor time-microenvironment-activity patterns as well but the effects were rather heterogeneous across the seven cities. Exposure to second-hand tobacco smoke differed substantially across these cities. The heterogeneity of these factors across cities may reflect city-specific characteristics but selection biases in the sampled local populations may also explain part of the findings. Determinants of time-microenvironment-activity patterns need to be taken into account in exposure assessment, epidemiological analyses, exposure simulations, as well as in the development of preventive strategies that focus on time-microenvironment-activity patterns that ultimately determine exposures.
To study the association of indoor air quality and ventilation in Chinese homes with occupants’ sick building syndrome (SBS) symptoms, we performed a study in 32 homes over four seasons in Tianjin, China. Measured indoor environmental parameters were ventilation rate, and concentrations of volatile organic compounds (VOCs), formaldehyde, PM2.5, ultrafine particle and ozone. Occupants reported any sick building syndrome symptoms for the previous three months. Ventilation rates at night in bedrooms were 0.35 h⁻¹, 0.78 h⁻¹, 0.37 h⁻¹ and 0.41 h⁻¹ in spring, summer, autumn and winter, respectively. A low ventilation rate at night (below the median value of 0.45 h⁻¹) significantly increased the risk (adjusted odds ratio, aOR) of mucosal symptom to 2.65 (95% Confidence Interval (CI): 1.16–6.06). The aOR of ultrafine particle for mucosal symptom was 2.37 (95% CI: 1.02–5.49) and for dermal symptoms 3.96 (95% CI: 1.63–9.60). The aOR of ozone for dermal symptoms was 5.86 (95% CI: 1.19–28.99). Dry air perception that indicated a polluted indoor environment was a risk factor for SBS symptoms in Chinese homes.
As a strong oxidizing gas, ozone can damage the human respiratory tract and cardiovascular system. Aside from ambient outdoor ozone that enters buildings, indoor ozone emission devices (IOEDs) such as disinfectors, air purifiers, and printing devices are the primary source of indoor ozone. This review briefly presents the types and ozone emission mechanisms of IOEDs, the setups and procedures for measuring the ozone emission rate (OER) of IOEDs, and various equations for analyzing test results. This review also summarizes and compares the OERs of different IOEDs and analyzes the factors affecting the OER. The average OERs of in-duct air cleaners, ozone generators, room air purifiers, photocopiers, laser printers, and other small household devices are 62.8, 76.3, 4.6, 3.3, 0.8, and 0.4 mg/h, respectively. The OERs of in-duct air cleaners and ozone generators are generally larger than those of printing devices. The highest and lowest OERs of room air purifiers in the surveyed literature are 30.5 mg/h and 56 μg/h, respectively, with a difference of approximately 550 times. The ozone emission per unit paper for printing devices and per kilowatt hour for other IOEDs are also calculated and compared. In addition, the effects of the design and working mechanism of IOEDs on the OER are also discussed in detail. Users' operation and daily maintenance of an IOED and the OER test conditions can also affect the OER. Finally, analytical equations are used to compare the influence of the test result processing method on the OER for the same IOED.
Demand response (DR) can effectively manage electricity use to improve the efficiency and reliability of power grids. Shutting down part of operating chillers directly in central air-conditioning systems can meet the urgent power reduction needs of grids. But during the special events of fast DR, how to optimally control the active cold storage considering the indoor environment of buildings and the needs of grids at the same time is rarely addressed. A model predictive control (MPC) approach, with the features of shrunk prediction horizon, self-correction and simple parameter determination of embedded models, is therefore developed to optimize the operation of a central air-conditioning system integrated with cold storage during fast DR events. The chiller power demand and cooling discharging rate of the storage are optimized to maximize the building power reduction and meanwhile to ensure the acceptable indoor environment. Case studies are conducted to test and validate the proposed method. Results show that the proposed MPC approach can effectively handle the optimal controls of cold storage during DR events for required power reduction and acceptable indoor environment. Due to the feedback mechanism of MPC, the control performance is not negatively influenced by the simplified parameter identification of models, which will be convenient for real applications. While achieving the expected building power reduction for the power grid, the indoor environment is effectively improved in the DR events using the MPC and the maximum indoor temperature is reduced significantly without extra energy consumed.
This paper deals with the problem of cost-optimal operation of smart buildings that integrate a centralized HVAC system, photovoltaic generation and both thermal and electrical storage devices. Building participation in a Demand-Response program is also considered. The proposed solution is based on a specialized Model Predictive Control strategy to optimally manage the HVAC system and the storage devices under thermal comfort and technological constraints. The related optimization problems turn out to be computationally appealing, even for large-scale problem instances. Performance evaluation, also in the presence of uncertainties and disturbances, is carried out using a realistic simulation framework.
The emerging of building information modelling provides opportunities to break through the limitations of conventional building energy modelling such as tedious model preparation, model inconsistency and costly implementation, and promotes building energy modelling into the digital building design process. The method of using building information modelling for the building energy modelling process, named building information modelling-based building energy modelling has become a prevalent and attractive topic in both the research and the industry society in recent years. This paper presents an overall review on the building design process, and applications of building information modelling and building energy modelling in the design process. It also provides an in-depth review on the development of building information modelling-based building energy modelling methods and the development of prevalent informational infrastructures. Meanwhile, this literature review provides a special consideration on the maturity of building data transformation between building information modelling and building energy modelling for building energy simulation process, from the step 1 identifying the geometry, thermal properties of buildings to the step 6 the information and components for HVAC systems. In general, the current building information modelling-based building energy modelling methods are thoroughly evaluated and the trends for future developments are outlined. It is realised that the Building Information Modelling based Building Energy Modelling is particular appropriate for the early design stage, where the most suitable and cost effective approaches for energy efficient design can be integrated into the overall building design process.
Ozone is a reactive gas that can have negative health effects on human. Building materials can be significant sinks for indoor ozone, owing to the irreversible heterogeneous reactions between ozone and material surfaces. Therefore, the ozone removal on material surfaces is crucial for evaluating indoor ozone concentrations and human exposure. This paper presents a review of previous investigations on ozone removal on building materials. The reaction probabilities of common indoor building materials range from 10-8 to 10-4, and depend on the material chemical compounds and surface characteristics. The surface-treated materials are probably more important than the underlying material substrate in determining ozone deposition velocities. Ozone removal on material surface is also associated with the fluid mechanics near the surface. Reactions between ozone and unsaturated organic compounds that constituting or adsorbed on material surfaces may result in oxidized by-products yields, while inorganic materials usually exhibit negligible by-products yields. Besides, the ozone surface removal on building materials under various conditions, i.e. ozone concentrations, air flow conditions, relative humidity and temperature, are discussed. Ozone removal on building materials after short-term and long-term exposure to ozone is presented.
Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.
Visualizing energy-based data according to multiple perspectives and performance criteria is critical to understanding its spatiotemporal character, impacts on comfort, and meaning in the design decision-making process. This research examines how a virtual environment for design and analysis (VEDA) enhances design decision making through visualization techniques combining solar irradiation mapping with interactive virtual reality tools. A participant study investigates the ability of VEDA to create opportunities to better understand relationships between energy data and indoor environmental comfort. A discussion of the findings highlights applicability in architectural design and education, and the conclusions make recommendations for further development.
Building energy consumption makes up 40% of the total energy consumption in the United States. Given that energy consumption in buildings is influenced by aspects of urban form such as density and floor-area-ratios (FAR), understanding the distribution of energy intensities is critical for city planners. This paper presents a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Our results show that gradient boosting regression models perform the best at predicting commercial building energy consumption, and can make predictions that are on average within a factor of 2 from the true energy consumption values (with an r2 score of 0.82). We validate our models using the New York City Local Law 84 energy consumption dataset, then apply them to the city of Atlanta to create aggregate energy consumption estimates. In general, the models developed only depend on five commonly accessible building and climate features, and can therefore be applied to diverse metropolitan areas in the United States and to other countries through replication of our methodology.
A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, including those methods for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those methods for classification (K-mean clustering, self-organizing map and hierarchy clustering). The review results demonstrate that the data-driven approaches have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the context of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies. It also paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.
Real-time occupancy predictions are essential components for the smart buildings in the imminent future. The occupancy information, such as the presence states and the occupants’ number, allows a robust control of the indoor environment to enhance the building energy performances. With many current studies focusing on the commercial building occupancy, most researchers modeled either the occupancy presence or the occupants’ number without evaluating the model potentials on both of them. This study focuses on 1) providing a unique data set containing the occupancy for the offices located in the U.S with difference pattern varieties, 2) proposing two methods, then comparing them with four existing methods, and 3) both presence of occupancy and occupancy number are predicted and tested using the approaches proposed in this study. In detail, the paper develops a new moving-window inhomogeneous Markov model based on change point analysis. A hierarchical probability sampling model is modified based on existed models. They are additional compared to well-known models from previous researchers. The study further explores and evaluates the predictive power of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-h ahead, and 24-h ahead forecasts. The final results show that the proposed Markov model outperforms the other methods with a max 22% difference in terms of presence forecasts for 15-min, 30 min and 1-h ahead. The proposed Markov model also outperforms other models in occupancy number prediction for all forecast windows with 0.34 RMSE and 0.23 MAE error respectively. However, there is not much performance difference between models for 24-h ahead predictions of occupancy presence forecast.
Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.
It is important for building owners and operators to manage the electrical energy consumption of their buildings. As electrical energy is the major form of energy consumed in a commercial building, the ability to forecast electrical energy consumption in a building will bring great benefits to the building owners and operators. This paper provides a review of the building electrical energy consumption forecasting methods which include the conventional and artificial intelligence (AI) methods. The significant goal of this study is to review, recognize, and analyse the performance of both methods for forecasting of electrical energy consumption. Compared to using a single method of forecasting, the hybrid of two forecasting methods can possibly be applied for more precise results. Regarding this potential, the swarm intelligence (SI) method has been reviewed to be hybridized with AI. Published literature presented in this paper shows that, the hybrid of SVM and SI methods has indeed presented superior performance for forecasting building electrical energy consumption.
Some of the challenges to predict energy utilization has gained recognition in the residential sector due to the significant energy consumption in recent decades. However, the modeling of residential building energy consumption is still underdeveloped for optimal and robust solutions while this research area has become of greater relevance with significant advances in computation and simulation. Such advances include the advent of artificial intelligence research in statistical model development. Artificial neural network has emerged as a key method to address the issue of nonlinearity of building energy data and the robust calculation of large and dynamic data. The development and validation of such models on one of the TxAIRE Research houses has been demonstrated in this paper. The TxAIRE houses have been designed to serve as realistic test facilities for demonstrating new technologies. The input variables used from the house data include number of days, outdoor temperature and solar radiation while the output variables are house and heat pump energy consumption. The models based on Levenberg-Marquardt and OWO-Newton algorithms had promising results of coefficients of determination within 0.87–0.91, which is comparable to prior literature. Further work will be explored to develop a robust model for residential building application.
Solar Chimney is a potentially effective low carbon technique that uses solar energy to heat and cool buildings or to enhance ventilation. Proper design of a solar chimney requires a reliable model to estimate the airflow rate generated by the heat of solar irradiation. Existing analytical models ignore density variations either for the whole channel or across the channel gap. This paper presents a plume model based on energy balances and the thermal boundary theory and thus considered density variations in both horizontal and vertical directions. This plume model, expressed implicitly as a function of heat flux, can be solved easily through simple iterations. The performance of the model was verified by using experimental data in the literature. Results show that the plume model outperformed existing analytical models. Recognizing the challenges in airflow measurement, we suggest that further tests and calibrations of this plume model be done by using more concrete and accurate experimental data. Simple in form, the plume model has its potential in building simulation programs.
The energy performance gap has been realized to be a significant barrier to achieving low energy buildings. A predominant reason of the performance gap resides with the uncertainties of occupancy information, which leads to inappropriate design, unnecessary consumption and operation misconducts of occupants. Traditional methods of collecting occupancy information mainly rely on statistics from post-occupancy evaluations (Post-OE). However, there is a lack of use of pre-occupancy evaluation (Pre-OE) methods to understand the interaction between occupant behavior and building design alternatives. The aim of this paper is thus to develop a virtual reality (VR) integrated approach to improving occupancy information integrity in the building energy design process in order to close the performance gap. A Pre-OE design framework is first constructed to enable the use of VR technology to help building designers collect occupancy information and identify the design patterns that guide occupants to behave in the most energy-efficient way. The framework is then validated through a case study of the lighting control design of a typical residential flat in Hong Kong. The results suggest that the developed framework can facilitate designers in understanding occupants’ behavior and identifying design alternatives that guide occupants to perform in accordance with design intentions. The adoption of higher-level VR technologies and multi-criteria decision-making methods is advocated for future research.
Building information modeling (BIM) is one of the most promising recent developments in the architecture, engineering, and construction (AEC) industry. With BIM technology, an accurate virtual model of a building is digitally constructed. This model, known as a building information model, can be used for planning, design, construction, and operation of the facility. It helps architects, engineers, and constructors visualize what is to be built in a simulated environment to identify any potential design, construction, or operational issues. BIM represents a new paradigm within AEC, one that encourages integration of the roles of all stakeholders on a project. In this paper, current trends, benefits, possible risks, and future challenges of BIM for the AEC industry are discussed. The findings of this study provide useful information for AEC industry practitioners considering implementing BIM technology in their projects.
This table contains data on the energy trade, supply and consumption of coal, oil, gas, electricity, heat, combustible renewables and waste, expressed in thousand tonnes of oil equivalent (ktoe).
The “Virtual Design Studio (VDS)” is a software platform currently under development in support of an integrated, coordinated and optimized design of buildings and their energy and environmental systems. It is intended to assist collaborating architects, engineers and project management team members throughout from the early phases to the detailed building design development. The platform helps to facilitate the workflow and the processing of information in combination with appropriate, task-based performance simulation tools as further analyzed in Part 2 of this study (DOI: 10.1007/s12273-013-0111-1). The present paper summarizes how VDS relates to the building design process and its typical project stages, performance-based design considerations and respective performance optimization strategies. It outlines the methodology and scope for the organization, implementation and respective requirements for the VDS platform development based on the interdisciplinary design needs. Part 2 will present the methodology for the systems integration and software implementation of VDS.
Thermal comfort has been discussed since 1930s. There have been two main approaches to thermal comfort: the steady-state model and the adaptive model. The adaptive model is mainly based on the theory of the human body's adapting to its outdoor and indoor climate. In this paper, besides the steady-state model, three adaptive thermal comfort standards are comprehensively reviewed: the American ASHRAE 55-2010 standard, the European EN15251 standard, and the Dutch ATG guideline. Through a case study from the Netherlands, these standards are compared. The main differences discussed between the standards are the equations for upper and lower limits, reference temperatures, acceptable temperature ranges and databases.
The House Simulation Protocol document was developed to track and manage progress toward Building America's multi-year, average whole-building energy reduction research goals for new construction and existing homes, using a consistent analytical reference point. This report summarizes the guidelines for developing and reporting these analytical results in a consistent and meaningful manner for all home energy uses using standard operating conditions.
Finite difference or finite element methods reduce transient multidimensional heat transfer problems into a set of first-order differential equations when thermal physical properties are time invariant and the heat transfer processes are linear. This paper presents a method for determining the exact solution to a set of first-order differential equations when the inputs are modeled by a continuous, piecewise linear curve. For long-time solutions, the method presented is more efficient than Euler, Crank-Nicolson, or other classical techniques.
The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.
In space applications, the main concern of particle deposition arises from the undesirable effects of surface obscuration on contamination-sensitive surfaces. The development of effective mitigation strategies to minimize particulate contamination requires the understanding of particle transport and deposition as well as the associated physical factors affecting the processes. The knowledge gleaned from the state-of-the-art literature review presented here can be applied to an enclosure scale as small as a spacecraft payload cavity, or as large as a clean room in a manufacturing/processing facility. The insights from studying the physical processes that influence the rate of particle deposition within an enclosure are expected to provide a strong scientific foundation to benefit various aspects of aerospace contamination control needs.
In recognition of fundamental changes in the way governments approach energy-related environmental issues, the IEA has prepared this publication on CO2 emissions from fuel combustion. This annual publication was first published in 1997 and has become an essential tool for analysts and policy makers in many international fora such as the Conference of the Parties.
The data in this book are designed to assist in understanding the evolution of the emissions of CO2 from 1971 to 2010 for more than 140 countries and regions by sector and by fuel. Emissions were calculated using IEA energy databases and the default methods and emission factors from the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Lower costs and improved performance of sensors, controllers, and networking is leading to the development of smart building features, such as continuous performance monitoring, automated diagnostics, and optimal supervisory control. For some of these applications, it is important to be able to predict transient cooling and heating requirements for the building using inverse models that are trained using on-site data. Existing inverse models for transient building loads range from purely empirical or “black-box” models to purely physical or “white-box” models. Generally, black-box (e.g., neural network) models require a significant amount of training data and may not always reflect the actual physical behavior, whereas white-box (e.g., finite difference) models require specification of many physical parameters. This paper presents a hybrid or “gray-box” modeling approach that uses a transfer function with parameters that are constrained to satisfy a simple physical representation for energy flows in the building structure. A robust method is also presented for training parameters of the constrained model, wherein initial values of and bounds on physical parameters are estimated from a rough building description, better estimates are obtained using a global direct search algorithm, and optimal parameters are identified using a nonlinear regression algorithm. The model and training method were extensively tested for different buildings and locations using data generated from a detailed simulation program. The approach was also tested using data from a field site located near Chicago, Illinois. It was found that one to two weeks of data are sufficient to train a model so that it can accurately predict transient cooling or heating requirements.
Epidemiologic evidence indicates a relationship between outdoor particle exposure and adverse health effects, while most people spend 85–90% of their time indoors, thus understanding the relationship between indoor and outdoor particles is quite important. This paper aims to provide an up-to-date revision for both experiment and modeling on relationship between indoor and outdoor particles. The use of three different parameters: indoor/outdoor (I/O) ratio, infiltration factor and penetration factor, to assess the relationship between indoor and outdoor particles were reviewed. The experimental data of the three parameters measured both in real houses and laboratories were summarized and analyzed. The I/O ratios vary considerably due to the difference in size-dependent indoor particle emission rates, the geometry of the cracks in building envelopes, and the air exchange rates. Thus, it is difficult to draw uniform conclusions as detailed information, which make I/O ratio hardly helpful for understanding the indoor/outdoor relationship. Infiltration factor represents the equilibrium fraction of ambient particles that penetrates indoors and remains suspended, which avoids the mixture with indoor particle sources. Penetration factor is the most relevant parameter for the particle penetration mechanism through cracks and leaks in the building envelope. We investigate the methods used in previously published studies to both measure and model the infiltration and penetration factors. We also discuss the application of the penetration factor models and provide recommendations for improvement.
The rapidly growing world energy use has already raised concerns over supply difficulties, exhaustion of energy resources and heavy environmental impacts (ozone layer depletion, global warming, climate change, etc.). The global contribution from buildings towards energy consumption, both residential and commercial, has steadily increased reaching figures between 20% and 40% in developed countries, and has exceeded the other major sectors: industrial and transportation. Growth in population, increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure the upward trend in energy demand will continue in the future. For this reason, energy efficiency in buildings is today a prime objective for energy policy at regional, national and international levels. Among building services, the growth in HVAC systems energy use is particularly significant (50% of building consumption and 20% of total consumption in the USA). This paper analyses available information concerning energy consumption in buildings, and particularly related to HVAC systems. Many questions arise: Is the necessary information available? Which are the main building types? What end uses should be considered in the breakdown? Comparisons between different countries are presented specially for commercial buildings. The case of offices is analysed in deeper detail.
The methodology to predict building energy consumption is increasingly important for building energy baseline model development and measurement and verification protocol (MVP). This paper presents support vector machines (SVM), a new neural network algorithm, to forecast building energy consumption in the tropical region. The objective of this paper is to examine the feasibility and applicability of SVM in building load forecasting area. Four commercial buildings in Singapore are selected randomly as case studies. Weather data including monthly mean outdoor dry-bulb temperature (T0), relative humidity (RH) and global solar radiation (GSR) are taken as three input features. Mean monthly landlord utility bills are collected for developing and testing models. In addition, the performance of SVM with respect to two parameters, C and ɛ, was explored using stepwise searching method based on radial-basis function (RBF) kernel. Finally, all prediction results are found to have coefficients of variance (CV) less than 3% and percentage error (%error) within 4%.
We conducted a re-analysis of data supplied by the New Buildings Institute and the US Green Buildings Council on measured energy use data from 100 LEED-certified commercial and institutional buildings. These data were compared to the energy use of the general US commercial building stock. We also examined energy use by LEED certification level, and by energy-related credits achieved in the certification process. On average, LEED buildings used 18–39% less energy per floor area than their conventional counterparts. However, 28–35% of LEED buildings used more energy than their conventional counterparts. Further, the measured energy performance of LEED buildings had little correlation with certification level of the building, or the number of energy credits achieved by the building at design time. Therefore, at a societal level, green buildings can contribute substantial energy savings, but further work needs to be done to define green building rating schemes to ensure more consistent success at the individual building level. Note, these findings should be considered as preliminary, and the analyses should be repeated when longer data histories from a larger sample of green buildings are available.
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.
Simple and effective building energy models are essentially needed for many applications, such as building performance diagnosis and optimal control, etc. The energy model involves two important parts of building components, i.e., building envelopes and building internal mass. This paper presents a methodology for parameter optimization of 3R2C thermal network model of building envelopes (composed of three resistances and two capacitances) based on frequency domain regression using genetic algorithm (GA). First, the theoretical frequency characteristics of heat transfer through building envelope are calculated using detailed physical description within the frequency range of concern. Second, the frequency characteristics of the simplified 3R2C model are calculated with random values of individual resistances and capacitances which constrain to total thermal resistance and capacitance. Then, the errors between the theoretical frequency characteristics and the frequency characteristics of the simplified model are calculated. Finally, GA estimator is developed to optimize the parameters of the simplified model, allowing the frequency responses of the simplified model match the actual heat transfer through building envelope the best. Various case studies are conducted also to validate the parameter optimization method of the simplified 3R2C model. The accuracy of simplified models for constructions of different weights is studied.