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Equivalence between the load curve and the free-running temperature in energy estimating methods
Abstract
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.
... Nevertheless, steady-state methods based on temperature [2,3] or on heating/cooling curve [4] can be adapted to characterize the dynamic behaviour if the frequency or probability distributions is considered [5]. The building free-running temperature characterizes the thermal building behaviour and the frequency distribution of the outdoor temperature to describe the climate [6]. The free-running temperature method is similar to the bin method [7] and it can be demonstrated that the degree-hours as used in the bin method can be expressed as a function of the free-running temperature [5]. ...
... The free-running temperature, T fr , is the indoor temperature when no energy is supplied by the heating or cooling system and the air change rate is kept at its nominal value (natural or mechanical ventilation without any air conditioning), i.e., no additional air change rate for free-cooling [5,6]. ...
... where the bar indicates mean values in a bin around T o , which are obtained from dynamically variables [5]. These dynamically variables may be expressed in the form of a transfer function having a static and a dynamic gain [6]. Eq. (2) is valid when the variables are around the nominal values. ...
Free-cooling by ventilation is one of the most energy efficient techniques for cooling. When ventilation is used for cooling, variable airflow rates should to be used in order to achieve comfortable room temperatures and to minimize the energy demand for mechanical ventilation. Thus, free-cooling, requires, obviously, the existence of a potential for cooling and needs control mechanisms for the airflow. In this study, the free cooling potential by ventilation for office buildings is evaluated by the free-running temperature. The free-running temperature approach is based on the energy balance of heat gains and losses. It is adapted to evaluate the potential for free cooling by ventilation of office buildings for which the gains through the walls are negligible as compared to the internal and solar gains. The free-running temperature of each office room considers solar and internal heat gains, outdoor temperature, indoor temperatures and ventilation air flow rates. The approach is applied to 14 office rooms in a passively cooled office building in Germany and is used to estimate the potential and to evaluate the total energy saving by free cooling by ventilation. The good fit between monitoring data and calculation procedure proves that the free cooling potential can be accurately estimated by using the difference between the comfort limits, i.e. the target value of the indoor temperature, and the free-running temperature.
... Another approach use statistical correlation methods, related to steady-state thermal balance. In these methods, the dynamics are taken into account by considering a statistical distribution around the mean values ( [3], [8], [9], [12], [15], [16], [22]). ...
... kWh for simulation time step of 1 h and 924.7185 kWh for 15 min). This good result for overall energy estimation is due to the filter behavior of the building model [8]. Another disadvantage of the current procedure is that the load is not optimal: the algorithm does not anticipate the solar radiation and free gains in order to set the load to zero before their effect shows up. ...
... command sequence) for set-point tracking to get the total power. 8 Choose the maximal value of the command to obtain the maximum load. ...
... Generally, system identification and regression techniques can be used to identify empirical models suitable for control [156,168,173,184,186,196,210,236,251], such as Auto-Regressive with Exogenous Inputs (ARX), Auto-Regressive with Moving Averages and Exogenous Inputs (ARMAX) or others. In parallel, regression techniques can be used to predict building energy performance as a function of relevant factors [234,238,239,[253][254][255]. Linear regression and system identification, as described in the previous section, can be used to obtain simplified physical models, thus establishing a potential continuity between black-box and grey-box approaches, while ANN or SVM cannot be used for this purpose [251]. ...
... While "active" storage systems (e.g. water storage tank, batteries, etc.) [256,257] can be operated according to optimal dynamic energy management strategies [213,252,258] and "passive" thermal storage properties of the building fabric can be exploited by means of optimized strategies [203,253,259]. In the following sections the fundamental issues with respect to load management and performance monitoring will be described. ...
The pressing global environmental issues are fostering a rapid change in the energy and sustainability policies for the built environment. New paradigms are emerging, such as “Nearly Zero Energy Building” (nZEB), and resource efficiency is progressively becoming a crucial topic in the building sector, implying an appropriate consideration of performance over the whole life cycle. However, empirical evidences show how, very often, the gap between the predicted (design phase) and measured (operation phase) performance is very large, due to errors committed during all the phases of building life cycle. This performance gap determines a problem of credibility in the building industry and, more in general, in sustainability oriented practices. Therefore, design and operation practices should evolve in order to be able to cope with performance uncertainty determined, for example, by evolution of climate conditions, variability of behavioural patterns and performance degradation of technological components.
... These formulas have then been manipulated to obtain the regression models, described in details in Tables 2 and 3. By using the output from the simplest regression model, we can calculate the building balance-point temperature and the average free-running temperature, θ fr (Ghiaus, 2006). ...
... The correlation between outdoor air temperature and free-running temperature is also particularly interesting, for example to explore graphically and analytically the potential of freecooling and variability in set-points of the internal air temperature with respect to different comfort models (Ghiaus, 2006). The data obtained are plotted in Fig. 5. ...
The sustainability of the built environment largely depends on its energy and environmental performances. The overall objective, across the different phases of the building life cycle is to improve building and system performances in terms of economics, comfort, environmental impact and durability.
Several modelling methodologies have been developed in order to evaluate the energy performance of buildings. Generally, every modelling methodology responds effectively to some specific tasks, but there exists a lack of integration in particular with respect to the cross-disciplinary role of data. Given the multi-scale and multi-objective nature of the problem of optimization of the energy and environmental performances of the built environment, an appropriate synthesis and integration process in modelling methodologies has to be identified, addressing realistically the uncertainties inherently present in modelling strategies.
Visualization and data analysis techniques are successfully used in a wide variety of applications, both in theoretical and applied domains, but questions remains about their robustness, efficiency and applicability to the problems introduced before. The paper aims to analyse critically these topics by means of case studies, showing a possible path to create a multi-scale methodology able to synthesize all the relevant aspects.
... The aim of this study is to present a general parametric method for evaluating building heating demand that will be useful in the further development of decision tools for integrated building design. Such parametric models, obtained by regression from dynamic simulation results, combine the speed of simplified models and the accuracy of dynamic models [6,7]. ...
... JðtÞ tb LðtÞ tb À T is À T oe;tb Á À a e;tb E e;tb h e;tb ! Dt (6) where J 0,tb and J(t) tb are the constant and the dimensionless time-dependent components of the linear thermal bridge coefficient J tb , respectively, L 0,tb and L(t) tb are the constant and the dimensionless time-dependent components of a thermal bridge of length L tb respectively, T oe,tb is the outdoor environment temperature to which the thermal bridge is exposed, and a e,tb , h e,tb , and E e,tb are the solar absorbance, combined heat transfer coefficient, and solar irradiance, respectively, associated with an external surface with a length of L tb and a width of 1 m. For a window, the computation of the transmission heat transfer does not include the effect of solar absorption, which is considered from this point on in the radiative amount of heat. ...
In this study, we describe a novel method for evaluating building heating demand based on a statistical approach. Our aim is to support integrated building design by providing fast modeling with accuracy close to that of dynamic simulations. A general parametric model encompassing overall building design is proposed based on the analysis of heat transfer. The method is subsequently applied to evaluate the heating demand of a single-family house in a cold French climate. Several polynomial functions are derived from the general model as functions of the amounts of heat transferred by different mechanisms and the physical and geometric building parameters. The model is identified with a small number of dynamic simulations using the design of experiments. The model illustrates how the weighting factors for the various amounts of heat are much higher in cold climates than in hot ones. We demonstrate that building heating demand can be accurately analyzed using the design parameters of the developed model. This analysis highlights the potential of this approach for supporting building energy designers in the choice of energy-efficient solutions.
... The possibility of using the central heating installation in those seasons when heat is not usually supplied; something which is generally impossible with most other systems. 11. Integration of all the necessary systems for settlements and regulation into one device which minimizes total investment costs and simplifies use of the system. ...
... In order to define the domain for heating, the free-running temperatures are calculated from Eq. (1), which was obtained by Ghiaus [11]. Generally, the free-running temperature, Tfr, is the indoor temperature when neither heating nor cooling is used and the air permeability of the building is kept at the winter value, i.e. the windows are closed [12]. ...
Nowadays, modern heat supply technologies are preferred by the decentralized municipal sector because they considerably reduce heat transfer losses. One such solution is a heating system using residential thermal stations (RTS). The advantages of a heating system with RTS, as compared with hot water storage vessels, include stabilizing heat costs, saving energy and a decrease in heat transfer losses.This paper presents the results of an experiment investigating heat consumption in a residential building using RTS. The building, located in Lublin, Poland, was supplied by the local district heating network. The energy consumption was monitored from April 2007 to April 2009. The efficiency of this system was 71.4% during the period when heat was required (winter) and 61.5% during the summer; an annual average efficiency of 67.1%. The energy consumption of the space heating system varied from 0.03 to 0.53GJm−2 of the flat's surface area, with the average value being 0.22GJm−2. The influences of the location of the flat within the building and the surface area of the flat on the quantity of heat required for space heating were analysed. Specific attention was paid to the occurrence of local heat flows between flats.
... Another approach use statistical correlation methods, related to steady-state thermal balance. In these methods, the dynamics are taken into account by considering a statistical distribution around the mean values [7][8][9][10][11][12][13]. ...
... kWh for simulation time step of 1 h and 924.7185 kWh for 15 min). This good result for overall energy estimation is due to the filter behavior of the building model [8]. ...
A building is permanently in thermodynamic non-equilibrium due to changing weather, free gains and indoor temperature set-point. Load calculation in dynamic conditions is an essential goal of building energy simulation. This paper demonstrates that the load calculation is a control problem. Supposing that the thermal model of the building is linear and that the model of the building, the weather conditions and occupational program are known in the design stage, the paper proposes an unconstrained optimal control algorithm which uses feed-forward to compensate the weather conditions and model predictive programming (MPP) for set-point tracking. MPP is obtained by modifying the dynamic matrix control (DMC), a variant of model predictive control (MPC).The peak load depends on the set-back time of the indoor temperature: smaller the set-back time, larger the peak load, but smaller energy consumption. Then, the choice of the weighting coefficients in the model predictive programming may be done on economical considerations.
... In such a case, the analyst may try to show the cumulative influence of passive design measures on the floating or free-running temperature; that is, how the interior temperature evolves without active thermostatic control. With the frequency and intensity of heatwaves increasing all over the world [49][50][51], the free-running temperature provides a basis for sizing cooling plants [52][53][54] but also indicates whether interior conditions will stay safely within physiological limits for heat-stress, particularly when there is a blackout or when occupants cannot afford to run or install mechanical cooling. Comparing the free-running temperature to thresholds for adaptive comfort and dangerous heat-stress can, therefore, indicate the thermal resilience of a proposed design [55][56][57][58]. ...
The topic of study is how to choose primary materials for minimal environmental impact while integrating as many functions into them as possible. Values for mathematical ratios are found, showing how to optimize the proportions of a building made with internal thermal mass. The mass is coupled with buoyancy ventilation in a natural thermal feedback cycle, driven by the daily swing of ambient temperature. This way, the primary material controls the structure, temperature, and ventilation. The ratios give the balance of dimensions needed to keep the thermal exchanges optimally synchronized. On one side of the balance, there is the ventilation openings and the height of the building. On the other side of the balance, there is the surface area, thickness, and thermal properties of the mass. Examples show how to take these ratios and materialize them into possible design options. That is, how they can shape the development of an architectural concept from part to whole, including the spatial organization of the building. The results suggest that thin-shell structures of minimum weight, and even timber buildings, can be optimally tuned.
... establish consensus and steer the activities of other project contributors and stakeholders. In the early 48 stages of design, architects develop a range of volumetric forms to facilitate discussion with project 49 contributors and stakeholders. These so-called "massing studies" do not need to be geometrically dimensionless ratios. ...
What proportions should a thermally massive building have? How should the thermal mass be distributed? Should the "massing" change with the choice of material? This paper shows how to optimize the physical proportions of a building so that it synchronizes ambient heat exchanges in a natural feedback cycle. The internal mass is thermally coupled with buoyancy ventilation; the cycle is driven by the daily swing of outdoor temperature. Tripling up functions in this way—so that structural materials can reliably cool and power the ventilation for buildings—could help decarbonize the construction industry and provide an effective strategy for adapting to life-threatening heatwaves. Based on harmonic analysis, the method allows designers to thermally tune the form and mass of a building to meet chosen targets for temperature and ventilation in free-running mode. Once the optimal balance of exchange rates is known, design teams can proportionally vary the building height and ventilation openings against the surface area and thickness of an internal thermal mass. The possible permutations are infinite but parametrically constrained, allowing teams to fairly compare the functional and environmental credentials of different construction materials while they produce and evaluate preliminary options for organizing the exterior form and interior spaces of a building. An example study suggests that thin-shell structures of minimum weight, and even timber buildings, may be optimally tuned to produce ample ventilation and temperature attenuation.
https://www.preprints.org/manuscript/201907.0178/v2
... The top three topics make up over 60% of research output (see Figure 4). The authors found the relatively low volume of research for MV within buildings (Gonzalez and Zamarreno 2005), FL and ML (Rutishauser, Joller, and Douglas 2005), ML (Ghiaus 2006), NN (Ekici and Aksoy 2009), NN (Yokoyama, Wakui, and Satake 2009), ML (Hong, Koo, and Jeong 2012), ML (Han, Lee, and Crespi 2014), ES and NN (Howell, Rezgui, and Yuce 2014), PR and ML (Yu, Haghighat, and Fung 2016), NN and GA (Yuce and Rezgui 2017), FL (Ain et al. 2018), GA (Grygierek and Ferdyn-Grygierek 2018), DL, NN and ML (Konstantakopoulos et al. 2018), DL, NN and ML (Mynhoff, Mocanu, and Gibescu 2018), NN, ML and GA (Reynolds et al. 2018), NN and ML (Yuan et al. 2018), FL and NN (Allen, Rubaai, and Chawla 2016), NN and FL (Dalamagkidis and Kolokotsa 2008), NN and GA (Ferreira et al. 2012), GA and FL (Navale and Nelson 2012), FL (Zhang et al. 2012), NN (Rahman, Selamat, and Ismail 2013), NN, FL, and GA (Shaikh et al. 2014), GA (Shaikh et al. 2016), ML (Jiang et al. 2017), GA (Ullah, Faheem, and Kim 2017), ML (Godina et al. 2018), GA, NN, and FL (Ahmad et al. 2016), ML and NN (Ahmad, Mourshed, and Rezgui 2017), FL (Ghadi, Rasul, and Khan 2016), FL (Wang, Yang, and Wang 2010), NN (Doukas et al. 2007), PR (Dong and Andrews 2009), ML (Carreira et al. 2018), ML (Wong and Li 2008), FL and GA (Callaghan et al. 2001, NLP, ML andFL (Schumann et al. 2011 (Ferreira et al. 2012), GA and FL (Navale and Nelson 2012), FL (Zhang et al. 2012), NN (Rahman, Selamat, and Ismail 2013), NN, FL, and GA (Shaikh et al. 2014), GA (Shaikh et al. 2016), ML (Jiang et al. 2017), GA (Ullah, Faheem, and Kim 2017), ML (Godina et al. 2018), GA, NN, and FL (Ahmad et al. 2016), ML and NN (Ahmad, Mourshed, and Rezgui 2017), FL (Ghadi, Rasul, and Khan 2016), FL (Wang, Yang, and Wang 2010), NN (Doukas et al. 2007), PR (Dong and Andrews 2009), ML (Carreira et al. 2018), ML (Wong and Li 2008), FL and GA (Callaghan et al. 2001), GA and FL (Kolokotsa et al. 2002), ES (Dragomir 2014), FL (Dounis andCaraiscos 2007, NLP andML (Schermerhorn andScheutz 2008) Safety Any research conducted for the purpose of providing improved security services and/or reducing risk of harm to occupants within a non-residential building Reduce risk of harm for occupants and promote a secure environment ML (Wong and Li 2008), FL and GA (Callaghan et al. 2001), MV (Liu, Makar, and Kim 2001), MV (Chiu 2004), PR and ML (Kyriacou et al. 2017), MV, NN and FL (Mahdipour and Dadkhah 2014) (Continued) especially surprising considering there is an abundance of applications for MV to perform people counting, space optimisation, and security systems within buildings. ...
The use of AI technologies in Smart Buildings is increasing as there are wide-scale benefits that can be derived from improving the efficiency of a building's operation and management. Buildings currently account for around 40% of global energy use, with AI technologies offering the opportunity to significantly reduce energy consumption through better automation, control, and reliability. These technologies can also be utilised to improve the safety and comfort of building occupants. This paper provides a literature review of research that has been conducted into AI technologies for use in Smart Buildings across the major AI topics including expert systems, fuzzy logic, genetic algorithms, machine learning, machine vision, natural language processing, neural networks, and pattern recognition. This process resulted in the determination that the volume of research conducted to-date is skewed towards some AI topics such as machine learning, neural networks and pattern recognition over other topics such as deep learning and natural language processing. It has also served to provide insights into which areas are likely to benefit the most from focusing future research efforts in that direction.
... Individual building modelling (Nouvel et al., 2015), synthetic assessments Degree day models (De Rosa et al., 2014) Theoretical assessment of urban areas (Rode et al., 2014) Models for assessing energy needs assist (Zhao & Magoulès, 2012) Models of limits of energy consumption reduction (Cullen et al., 2011) Scenario models Two-dimensional and three-dimensional urban extent data can provide information on buildings and urban form, relevant to energy consumption Visual satellite imagery, particularly from the AVHRR and MODIS instruments. Nightlight data (NOAA, 2018) Backscatter data for vertical extent studies: NASA SeaWinds and DLR TerraSAR-X Energy/fuel usage data from national data bases by sector Case studies for individual buildings or cities (e.g., Santamouris et al., 2001;Frank, 2005;Ghiaus, 2006;Persson et al., 2006;Inard et al., 2011;Zangheri et al., 2014) Medium ...
Non-technical summary
Manhattan, Berlin and New Delhi all need to take action to adapt to climate change and to reduce greenhouse gas emissions. While case studies on these cities provide valuable insights, comparability and scalability remain sidelined. It is therefore timely to review the state-of-the-art in data infrastructures, including earth observations, social media data, and how they could be better integrated to advance climate change science in cities and urban areas. We present three routes for expanding knowledge on global urban areas: mainstreaming data collections, amplifying the use of big data and taking further advantage of computational methods to analyse qualitative data to gain new insights. These data-based approaches have the potential to upscale urban climate solutions and effect change at the global scale.
... The rational method commonly used for papers dealing with optimal control is the discomfort rate calculated from a temperature interval. It is used by Ghiaus [17] , Sourbron [18] and Gweder et al. [19] for the control of heating floors and ceilings. Thus, a discomfort rate between 19 °C and 23 °C is chosen during the occupied periods. ...
Thermally Activated Building Systems (TABS) are difficult to control due to the time lag between the control sending and the response of the indoor temperature. Energy management of systems having such a high inertia can be improved by optimizing the restart time thanks to both occupancy and weather anticipation. Predictive control is suitable for systems with numerous constrained inputs and outputs whose objective function varies over time such as buildings with intermittent occupancy. This work proposes to use a Model Predictive Control (MPC) for one TABS in particular: a floor heating system (FHS) of an experimental building. Conventional control techniques and a state of the art on the predictive control of FHS are presented. A complete control loop (sensor, actuator, controller) was implemented on an experimental room. The predictive controller that integrate the model selected in Part I is compared with two conventional control strategies. The energy saving potential of the predictive controller is confirmed by both local experimentation and simulation on three climates. The energy saving is close to 40% over the whole heating season with an improved or equivalent comfort situation compared to the other two reference strategies. The absolute gain is constant over the heating period but the most significant relative gains are obtained in the mid-season.
... Finally, this strategy can be extended up to hourly data regression [32,94]. Hourly consumption patterns, differently from the daily patterns, can show critical peak conditions and highlight the potential for free-cooling [95,96] and flexibility of operational setting with respect to comfort, ideally creating a continuity with Model Predictive Control strategies [64,97,98]. ...
Occupant’s behavioural patterns determine a significant level of uncertainty in building energy performance evaluation. It is difficult to account for this uncertainty in the design phase when operational and occupancy profiles are unknown. The relevant “performance gap” usually encountered between simulated and measured energy performance is clearly connected to biased assumptions in modeling, especially in the initial design phase. A probabilistic modeling approach is proposed to improve simulation reliability and robustness with respect to variability in occupancy patterns. The case study presented is the eLUX lab of the “Smart Campus” of Brescia University in Italy. Occupancy dependent input parameters such as air change rates (i.e. mechanically controlled ventilation) and internal heat gains (i.e. due to people, lighting and appliances) are described by means of probability distributions to obtain probabilistic thermal demand and load profiles as output. Probabilistic results enables a more reliable identification of energy saving strategies (operational and environmental settings) with respect to highly variable operating conditions. Further, simulation data are processed to obtain a weather-adjusted energy demand visualization, suitable for establishing a continuity between modeling in design and operation phases, with calibration purpose. Calibrated energy models can be used for several specific tasks in the operation phase, in particular condition monitoring, fault detection and diagnosis, supervisory control and energy management. For the case study presented, a detailed data acquisition scheme has been designed to enable an effective monitoring activity in the operation phase, aimed at experimenting model-based approaches for the tasks reported. The proposed research is the point-of-departure for a general activity aimed at assessing critically the issues of reliability and robustness of simulation results obtained with conventional modeling approaches, in particular with respect to occupants’ behaviour, exploiting at the same time the possibility of using measured data as a direct feedback to promote behavioural change.
... Finally, this strategy can be extended up to hourly data regression [32,94]. Hourly consumption patterns, differently from the daily patterns, can show critical peak conditions and highlight the potential for free-cooling [95,96] and flexibility of operational setting with respect to comfort, ideally creating a continuity with Model Predictive Control strategies [64,97,98]. ...
Occupant’s behaviour determines a significant level of uncertainty in building energy use. The relevant “performance gap” usually encountered between energy performance simulation with standard design conditions and actual energy performance is clearly connected to the biased assumptions made, especially for the parameters that can have a large variability. The paper deals with the variability of energy performance determined by occupants’ behaviour. The operation parameters related to occupancy profiles, such as air change rates and internal heat gains, are investigated. A specific analysis of indoor air quality is carried out to verify the internal conditions of an education building, chosen as case study, the Smart Campus Demonstrator of Brescia University in Italy. The parameters related to occupancy and used in energy modelling are derived from realistic dynamic patterns generated stochastically. The data envelopment of the stochastic schedules is used to simulate the potential variability in daily and hourly energy consumption due to changing operational patterns and to highlight the “performance gap” with respect to standard simulation settings. The modelling approach proposed is conceived for an initial energy modelling phase, however it can be extended and validated during real time building operation, by implementing coherent performance monitoring and benchmarking practices. The research work constitutes the starting point of a more general activity aimed at integrating inverse modelling techniques in current design practices for building retrofit. The availability of a calibrated and validated building energy model is fundamental to explore accurately further efficiency intervention for the building.
... Total thermal resistance describes the relation between the indoor temperature and the 3 heat losses from conduction and ventilation. These values, expressed in the form of global 4 thermal conductance, may be used to calculate the energy use from the load curve, which was 5 mentioned by Ghiaus [33,34]. 6 ...
The education of energy users is one of many possibilities to reduce the energy consumption in existing multifamily buildings. Unfortunately, to the best authors’ knowledge, there is no long term experimental evaluation of energy savings, which may be achieved in flats where heat cost allocators were installed.
This article presents the results of experimental research conducted during 17 heating seasons (from 1997/1998 to 2013/2014) in a multifamily building located in Poland. The heat cost allocators were installed in the right part of the building (part R) in 1996 and in the left part of the building of the same size (part L) in 2011. In the summer 2005, thermal renovation of external walls of the analysed building was made.
The energy consumption in part R of the building was on average 26.6% and 30.5% lower than in part L for the period before and after thermal renovation of external walls of the building, respectively. After the installation of the heat cost allocators in part L of the building, the amount of heat used for heating was also analysed. The comparison of saved energy to the cost of installing, reading, and maintaining the heat cost allocators was made.
... However, the heating/cooling curve and the free-running temperature are equivalent, which enables analysis of the building's heating, ventilation and cooling regimes by using a single concept. The advantage of this method is that the three main factors that influence the energy consumption of the building (the thermal behaviour of the building, the thermal comfort range and climate) are decoupled (Ghiaus, 2006b). Energy consumption for the whole range of variation of the outdoor temperature is: ...
... Another approach uses statistical correlation methods, related to steady-state thermal balance. In these methods, the dynamics are taken into account by considering a statistical distribution around the mean values (Bauer & Scartezzini, 1998;Ghiaus, 2006aGhiaus, , 2006bJaffal, Inard & Ghiaus, 2009;Pedersen, Stang & Ulseth, 2008;Rabl & Rialhe, 1992;Yu & Chan, 2005). ...
This thesis proposes methods and solutions to improve the choice and the optimal use of renewable energies in buildings. The heating load assessment is transformed into a control problem where the regulator calculates the optimal heating load of the building. The proposed regulator for this aim is Model Predictive Programming (MPP), which is obtained by modifying Model Predictive Control (MPC). The required information by MPP is a low order building model and data records of the local weather. Therefore, we propose a modelling method in which the detailed model of the building is projected on a reduced order model having its structure obtained from physical knowledge. For the control of the multi source system, we proposed a Building Energy Management System (BEMS) which is divided in two parts: the first for the building temperature control and the second for the source control. For building thermal control we utilize MPC, for which we propose a new cost function because the classical one does not minimize the energy consumption. The proposed cost function permits to maintain the thermal comfort with minimal energy consumption. We formulate this function such that it can be optimized by using Linear Programming (LP) algorithm. To be able to use LP we give a solution to linearization of the building model based on the physical knowledge, which permits to use the model on the entire operating range. For the source control, we propose a solution which takes into account the command given by MPC in order to use the energy resources more effectively. The proposed control system is evaluated and compared with two PID based BEMS, against comfort and energetic criteria. The evaluation is performed in emulation on a reference detached house. The obtained results show that the proposed control system always maintains the thermal comfort in the building, reduces the energy consumption and the wear and tear of the hydraulic and heat pumps from the heating system.
... In terms of system level properties, for example, the ability to store energy of the building fabric, related to its thermal inertia, can be effectively exploited in summer, to reduce cooling load [14] [15] and overheating [16]. It can be also successfully exploited in the intermediate seasons to obtain comfortable conditions in a ''passive'' way (acceptable internal conditions are guaranteed without the operation of technical systems) [17] [18] [19]. Thermal inertia affects the way in which a building reacts to changes in external and internal conditions influencing therefore its thermal load patterns (sensible load for heating and cooling); the potential advantage in terms of energy consumption given by higher effective thermal capacity depends on the specific intermittent operating conditions [20] [21]. ...
The reduction of energy consumption for heating and cooling services in the existing building stock is a key challenge for global sustainability today and buildings' envelopes retrofit is one the main issues. Most of the existing buildings' envelopes have low levels of insulation, high thermal losses due to thermal bridges and cracks, absence of appropriate solar control, etc.Further, in building refurbishment, the importance of a system level approach is often undervalued in favour of simplistic "off the shelf" efficient solutions, focused on the reduction of thermal transmittance and on the enhancement of solar control capabilities. In many cases, the importance of the dynamic thermal properties is often neglected or underestimated and the effective thermal capacity is not properly considered as one of the design parameters.The research presented aims to critically assess the influence of the dynamic thermal properties of the building fabric (roof, walls and floors) on sensible heating and cooling energy demand for a case study. The case study chosen is an existing office building which has been retrofitted in recent years and whose energy model has been calibrated according to the data collected in the monitoring process.The research illustrates the variations of the sensible thermal energy demand of the building in different retrofit scenarios, and relates them to the variations of the dynamic thermal properties of the construction components.A parametric simulation study has been performed, encompassing the use of calculation tools and performance metrics at the different scales of the building (envelope-zone-overall building) considering the realistic operating conditions assumed for the initial calibrated model and different climate conditions, typical of the Italian territory and, more in general, of the Southern European and Mediterranean area.
... To evaluate the performance of passive solar houses, the hours within a comfort range of 18À28 o C are counted as an index along with a survey given to the inhabitants [10]. Ghiaus has presented the concept of freerunning temperature to characterize the load curve of a building [11]. Using the free-running temperature, Christian Inard et al. have estimated the potential for free cooling by ventilation [12]. ...
The concepts of the energy saving equivalent (ESE) and energy saving index (ESI)
are presented in this paper to evaluate the performance of new materials and components in
passive buildings. The ESE represents the hypothetical energy that should be input to
maintain a passive room at the same thermal state as that when a particular material or
component is adopted. The ESI is the ratio of a particular material or component's energy
saving equivalent to the corresponding value of the ideal material or component that can .
... The free-running temperature is defined as the temperature of a free-running building without indoor climate controls, i.e. without heating, cooling and with the minimal air-changes per hour (Ghiaus, 2003). The free-running temperature can be obtained by simulation or, in the case of an existing building, by measurements or by using its relation with the load curve used in ASHRAE energy estimation methods (Ghiaus, 2006). The difference between the free-running temperature and the out- ...
... In [2] robust regression of the heat losses to the outdoor temperature is proposed for determining of the heat transfer coefficient, reporting improved predictions when data from different time-space locations are used. The equivalence between the regression-determined load curve and the free running temperature for energy estimations has been presented in [3], and the concept has been applied for determining of the free cooling potential of an office building [4]. In [5] K tot is determined from monthly data assuming consumption profiles of the unknown household electricity. ...
A method for determining the total heat loss coefficient, the effective heat capacity and the net solar gain of a building is presented. The method uses a linear regressions approach based on daily energy consumption combined with readily available meteorological data.The effective heat capacity of the building is evaluated by correlating the energy consumption and outdoor temperature changes from the previous day. The net solar gain of the building is assessed by analysing the data separated into groups by amount of daily solar irradiation. Corrected total heat loss coefficient is determined by explicitly including in the building's energy balance the accumulated heat and the solar gain.The method has been applied to the analysis of nine public buildings in Spain. An improvement of the estimated heat loss coefficient due to the corrections is observed. The effective heat capacity normalised by the building area is shown to be a useful indicator of the building operation, detecting continuous or intermittent heating.The estimated parameters in this study can enable specific benchmarking, detecting opportunities for energy savings and evaluating their potential. With the increasing implementation of smart metering technologies, the method is promising for application to the analysis of large building stocks.
... In order to evaluate the thermal comfort degree for indoor air temperature, the following new parameters I win (integrated discomfort degree for indoor temperature in winter) and I sum (integrated discomfort degree for indoor temperature in summer) are defined, which are similar with the parameters defined in our previous work [12,13]. Cristian Ghiaus presented the similar parameter for heating and for cooling in their papers [14]. ...
Free-cooling is understood as a means to store outdoors coolness during the night, to supply indoors cooling during the day in summer, while free-heating is understood as a means to store the solar radiation during daytime, to supply indoors heating during the night in winter. In principle, free-cooling or free-heating can make the indoor air temperature in the comfortable region all the year if the thermophysical properties of building envelope material are in the desired range (defined as ideal thermophysical properties in this paper). Those properties are obviously related to the outdoor climate condition, internal heat source intensity, building configuration, ventilation mode etc. For a given region and a given building, the critical values of those ideal thermal physical properties can be determined through modeling and simulation. Two parameters, Iwin and Isum, are defined to describe the overcool degree in winter and the overheat degree in summer, respectively. To illustrate, the critical values of thermophysical properties of building envelope of a building located in Beijing are obtained through modeling and simulation. The simulated results are validated with experiments. The model, the methodology and the results are helpful for selection of suitable building envelope materials and for design of energy efficient buildings.
... For example, data mining method is used to predict total building energy demand in [10] and to identify residential load in the smart grid context in [11]. A probabilistic approach combined with the free-running concept temperature are used to describe dynamic behavior by steady-state concepts and predict building energy use in [12]. Principal component regression (PCR) and partial least squares regression (PLSR) present the application of principal component analysis (PCA) in linear models. ...
The aim of the study was to identify driving variables of energy use in a low energy office building by integrating building energy management system (BEMS) and energy use data. To take a further step towards zero emission buildings, it is necessary to identify what contributes the most to building energy use. Further, the idea was to encourage a smart use of BEMS data for energy use analysis. Multivariable analysis was used for the data analysis. Databases of 76 and 41 variables respectively, which included occupancy level, control signals, and water and air temperatures, were used to explain heating, electricity, and fan energy use. The results showed that important variables were different for different months in the case of heating energy use. The total electricity and fan electricity use could be defined with the same variables in different months. The total electricity use could be defined by using occupancy level and input fan signals. The suggested approach could be used by building operators to identify opportunities for decreasing energy use and for energy use estimation when data are lost due to data transmission issues or other problems. A relationship between building information and energy use was established.
... Total thermal resistance describes the relation between the indoor temperature and the heat losses from conduction and ventilation. These values, in the form of global thermal conductance, may be used to calculate energy use from the load curve [18], but the free-running temperature may be a better alternative to the load curve in describing the thermal characteristics of the building [19]. The relations between the specific consumptions on the floors are not the same as the total thermal resistances for flats in different locations in the buildings being studied ( Table 2). ...
The residential sector consumes a considerable amount of energy for heating and preparing hot water. Little research has been conducted on heating systems in multifamily buildings under working conditions. This article presents the results of experimental research on three commonly used heating systems in multifamily buildings. A central heating and central domestic hot water system (system A), a decentralised system using residential thermal stations (system B) and a decentralised system using bi-functional gas boilers (system C) were analysed over a one year period. The systems analysed were in use in multifamily buildings in Lublin, Poland. The average annual efficiency for system A was 59.6%, that for system B was 70.1% and that for system C, 90.5%. The study determined the decrease in efficiency when the system was producing hot water only versus the system operating during the heating season. These decreases amounted to 20.7% for system A and 8% for system B. The amount of heat delivered and that lost in the respective heating systems were calculated and their relative advantages and disadvantages were identified. Special attention was paid to the amount of heat used for heating the flats of the buildings.Highlights► Experimental research on three heating systems in multifamily buildings are presented. ► The average annual efficiency for each system is determined. ► A system with decentralised heating and hot water preparation may reduce energy needs. ► The efficiency of heating system decrease when it produces only hot water.
... System identification can be based on inverse modelling using, e.g. neural networks, see for example [6][7][8] parametric studies based on statistical models, which is more widely used [9,10]. In the literature a variety of methods can be found. ...
The energy performance of buildings has become increasingly important in the general strive to reduce the overall energy use, which is manifested in the Energy Performance of Buildings Directive launched by the European Union. An important first step is of course to identify and address buildings that have an energy saving potential. In order to achieve this, robust methods for evaluation as well as reliable energy key figures are needed.For a large majority of multifamily buildings in Sweden the available data of the energy use originates from the property holder. Unfortunately, the data is often limited to the energy that the property holder is responsible for. Thus, information from the tenants about their household electricity use, indoor temperatures, number of residents, etc., is missing.In this paper an evaluation was conducted on monthly consumption data registered by the property holders for over 100 multifamily buildings/real estates in Sweden. The used approach, based on the energy signature method, was developed for evaluating the energy performance of multifamily buildings in terms of the overall heat loss coefficient, Ktot. To compensate for the missing data, different assumed consumption profiles have been used.The results shows that although the obtained value of Ktot for an individual building is rather sensitive to the assumed consumption profiles of household electricity, the sensitivity is reduced if the evaluation is made in relative and not absolute terms. Thus, the use of consumption profiles could be a successful way to circumvent the present lack of a detailed knowledge of a buildings total energy use. In addition, an evaluation in relative terms instead of absolute values is also more correct, since available data in almost all cases does not support the determination of the true value of Ktot. The use of an average consumption profile instead of a detailed knowledge is of course not desirable, but for fairly large buildings such an approach could be successful.
In order to further reduce the impact of climate fluctuations on the typical meteorological year (TMY) database, this paper introduces an ensemble empirical model decomposition method to extract the periodic fluctuation and random fluctuation data from outdoor climate data separately, and to construct a comprehensive description parameter that eliminates the influence of random fluctuation data. An innovative TMY based on the comprehensive description parameter was developed in six selected cities of different climate zones in China. Compared with the existing Chinese TMY development method and outdoor design parameters, it is found that the typical meteorological months (TMMs) of each city and the outdoor design parameters from the improved TMY database have changed to a certain extent. Through the correlation analysis between improved TMY database and the cumulative long-average meteorological data, it reveals that the improved TMY can better describe the local average climatic characteristics. Finally, this paper discusses the impact of the improved TMY on the building heat loss index and outdoor thermal comfort in different building shapes. The results demonstrate that the energy demand and outdoor thermal comfort analysis based on the improved TMY are closer to long-term averaged outdoor climate, and the calculation deviations compared with conventional method are reduced by 1.18%–21.08% and 53.42%–76.82% respectively. This research will refine outdoor climate data for building design and analysis.
Various thermal prediction methods and tools for conceptual building design have been rapidly developed in the past decades. However, most of these methods have some drawbacks to obsess their applications, such as heavily relying on very detailed information of building envelope components, and their thermal conductivity data with complicated computation models or formulas; and difficulties for architects to understand and judge the outcomes of such predictions in a conceptual design. Therefore, this research targets on developing a simplified method to predict the integrated thermal performance of a building with simplified envelope parameters and hourly free-running temperatures in the conceptual design stage. It categorizes the scattered design parameters into three combined thermal characteristic indexes and applies a serials of corresponding quasi-steady calculation methods for building thermal processes to predict hourly free-running temperatures. The interrelationships between these three combined indexes and two overall thermal performance indicators are verified via a sensitivity analysis, and are presented in two charts, which can easily be adopted in a conceptual building design. This method has been validated in an experimental case study. It can help architects to quickly find the thermal design performance of a designed building in the conceptual design stage.
What proportions should a thermally massive building have? How should the thermal mass be distributed? Should the "massing" change with the choice of material? This paper shows how to optimize the physical proportions of a building so that it synchronizes ambient heat exchanges in a natural feedback cycle. An internal mass is thermally coupled with buoyancy ventilation; the cycle is driven by the daily swing of outdoor temperature. Tripling up functions in this way—so that structural materials can reliably cool and power the ventilation for buildings—could help decarbonize the construction industry and provide an effective strategy for adapting to life-threatening heatwaves. Based on harmonic analysis, the method allows designers to thermally tune the form and mass of a building to meet chosen targets for temperature and ventilation in free-running mode. Once the optimal balance of exchange rates is known, design teams can proportionally vary the building height and ventilation openings against the surface area and thickness of an internal thermal mass. The possible permutations are infinite but parametrically constrained, allowing teams to fairly compare the functional and environmental credentials of different construction materials while they produce and evaluate preliminary options for organizing the exterior form and interior spaces of a building. An example study suggests that thin-shell structures of minimum weight, and even timber buildings, may be optimally tuned to produce ample ventilation and temperature attenuation.
The need to promote sustainable human settlements and to mitigate the spatial, demographic, social, economic, and environmental impacts, determined by the rapid global urbanization trend, is creating a concentration of research and development efforts in the built environment area. Considering the urbanization trend at the global level, cities constitute a priority for research and development in sustainability transitions, which should necessarily face techno- and socio economic problems. Energy use and technology affect sustainability in its fundamental components, society, environment, and economy. This chapter introduces readers to the concept of multi-level perspective modeling in sustainability transitions planning. It describes the most relevant characteristics and attributes of modeling techniques and data schemes for built environment performance modeling. The chapter also describes ongoing research on multi-scale computing for the built environment. Finally, it discusses the essential features of methodological and computational tools for the built environment.
Dans un contexte de raréfaction des énergies à bas coût et de lutte contre le changement climatique, le secteur du bâtiment est contraint à des objectifs de diminution de ses consommations d'énergie. Un contrôle adapté des systèmes énergétiques du bâtiment peut constituer un levier important pour tendre vers cet objectif.
Le but premier de ces travaux est la construction de modèles de faibles dimensions afin de pouvoir les embarquer dans un contrôleur pour améliorer la gestion énergétique. Ces modèles doivent être capables de s’enrichir des mesures disponibles sur site. Un bâtiment démonstrateur situé à l'Institut Universitaire de Technologie Génie Civil Construction Durable de Bordeaux sert de support au projet. Ce travail se décline en quatre volets. Le premier concerne la modélisation puisque des modèles légers basés sur l'analogie électrique et la représentation d'état sont proposés afin de décrire la dynamique du bâtiment sur un horizon de contrôle de quelques jours. Le deuxième volet concerne l'instrumentation du bâtiment puisque des campagnes de mesures courtes sont réalisées afin d'identifier les valeurs des paramètres du modèle de façon à minimiser l'écart modèle-mesure. Certains capteurs servent par la suite pour la gestion énergétique du bâtiment ; ces travaux posent donc également la question du jeu minimal de capteurs. Le troisième volet concerne la caractérisation des systèmes servant à piloter l'ambiance puisque dans une logique de contrôle optimal il faut être capable de relier l'effet de la commande sur la grandeur d'intérêt (température de confort). Le bâtiment démonstrateur comprend deux systèmes de chauffage : un plancher chauffant et des ventilo-convecteurs ainsi qu'une centrale de traitement d'air double flux pour le renouvellement d'air. Le dernier volet concerne la gestion énergétique avec l'utilisation d'un contrôleur prédictif embarquant l'un des modèles identifiés.
Ainsi l’objectif est de chercher à anticiper le pilotage d'un système capacitif à long temps de réponse, tel que le plancher chauffant, grâce à la connaissance des perturbations futures (occupation, météo) sur un horizon de prédiction de quelques heures. Un contrôle réactif est assuré par les ventilo-convecteurs en appoint. La gestion prédictive est comparée à des stratégies de gestion plus classiques en simulation et in situ avec le bâtiment démonstrateur.
L'originalité de ces travaux est de proposer une démarche pour la mise en place d'une boucle de contrôle complète (contrôleur/capteurs/actionneurs) et de montrer s'il existe un intérêt à la gestion prédictive de systèmes à long temps de réponse dans le domaine du bâtiment.
The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.
In sub-Saharan African countries, the energy context, the high urban growth, inadequate construction techniques and climate offer great potential for bio-climatic approach and sustainable construction particularly on the energy level. However, this potential and the use of passive cooling techniques are weakly explored. To do so, it requires a good knowledge of building’s behaviour and their adaptation to the climatic context. As part of our contribution to this issue, a study on the thermal behaviour of buildings is initiated by numerical simulation. Indeed the building’s energy performance simulation tools are becoming more essential in the building’s design processes and analysis. Investigations are conducted on a building built with typical materials in Burkina Faso by following two main approaches. In the first approach a model of the building is faced with measurements from field experiments on the building. A calibration methodology based on the sensitivity analysis and optimization has been applied for the comparison of results. This helped to calibrate the model and diagnostic studies are performed.In the second approach, investigations on methods which can help to improve the building performance are realized through the application building performance simulation as a design aid tool. Interoperability and optimization techniques are used to deal with passive cooling techniques and their impacts on the building thermal behaviour are assessed. Finally in this work analysis techniques and performance criteria are used to characterize and make recommendations on building designs for the tropical climate context.
In sub-Saharan African countries, the energy context, the high urban growth, inadequate construction techniques and climate offer great potential for bio-climatic approach and sustainable construction particularly on the energy level. However, this potential and the use of passive cooling techniques are weakly explored. To do so, it requires a good knowledge of building’s behaviour and their adaptation to the climatic context. As part of our contribution to this issue, a study on the thermal behaviour of buildings is initiated by numerical simulation. Indeed the building’s energy performance simulation tools are becoming more essential in the building’s design processes and analysis. Investigations are conducted on a building built with typical materials in Burkina Faso by following two main approaches. In the first approach a model of the building is faced with measurements from field experiments on the building. A calibration methodology based on the sensitivity analysis and optimization has been applied for the comparison of results. This helped to calibrate the model and diagnostic studies are performed.In the second approach, investigations on methods which can help to improve the building performance are realized through the application building performance simulation as a design aid tool. Interoperability and optimization techniques are used to deal with passive cooling techniques and their impacts on the building thermal behaviour are assessed. Finally in this work analysis techniques and performance criteria are used to characterize and make recommendations on building designs for the tropical climate context.
The sustainability of the built environment largely depends on its energy and environmental performances. The overall objective, across the different phases of the building life cycle such as design phase, construction phase, commissioning phase, operation phase and eventually refurbishment phase, is to improve building and system performances in terms of economics, comfort, environmental impact and durability.
Numerical simulation tools and optimization methods are needed to properly evaluate all the key performance indicators simultaneously, unveiling the existing gaps and identifying possible synergies and strategies in the performance estimation and decision-making processes for the building life cycle.
Further, several modelling methodologies have been developed in order to evaluate the energy performance of buildings. Generally, every modelling methodology responds effectively to some specific tasks, but there exists a lack of integration in the overall optimization process.
Given the multi-scale and multi-objective nature of the problem of optimization of the energy and environmental performances of the built environment, subject to economic and comfort constraints, an appropriate synthesis and integration process in modelling methodologies has to be identified, addressing realistically the uncertainties inherently present in every modelling strategy.
Data analysis and optimization techniques are successfully used in a wide variety of applications. Although these techniques have proven to be successful in both theoretical and applied domains, questions remains about their applicability for the problems introduced before. These questions involve primarily the robustness and efficiency of solutions procedures and the ability to identify relevant properties and to deal with large quantities of data.
The paper aims to analyse critically these topics by means of case studies, showing a possible path to create an integrated methodology able to synthesize all the relevant aspects previously mentioned
Since the first Chinese passive solar house was built in 1977, a large number of passive solar houses have been built within 20 years. However, many problems appeared during the long-term utilization process, such as poor heating effect, inconvenient operation control and incomplete design standards, which lead to the development of passive solar houses stagnant in the past decade. To solve the above- mentioned problems, technical improvement and free running temperature prediction methods of passive solar house were investigated in this study. As a case study, a two-year experiment was undertaken in an improved passive solar house, located in Dalian city, northeast China, and the performance of solar air collector was investigated in winter mainly. The experimental results showed the indoor-outdoor temperature differences were about 13.4-24.5 °C without auxiliary heat exchanger. According to the function of useful heat gain and solar irradiance obtained through regression analyses of experimental data, a simple free running temperature prediction formula of passive solar house for engineering application was obtained, which can be expressed as the linear superposition function of three main factors, including outdoor temperature, internal gains and solar irradiance. Taking the improved solar house as an example, a good agreement between monitored data and predicted data proved the feasibility of the prediction formula. In addition, weighed coefficients of influence factors in prediction formula were determined based on five typical cities in cold areas of China.
This study aimed to propose an optimization process for a free-form building shape in terms of the thermal load characteristic in the early design stage. Geometric modeling of a model free-form building was performed using a parametric design method with Rhinoceros. The model free-form building's surface was divided into finite elements by generating a mesh using Grasshopper, which is an add-in program of Rhinoceros. Geometric information was extracted from each finite element and used to estimate the heat gain and loss characteristics of the whole free-form building. A free-form building shape optimization process was proposed based on the genetic algorithm (GA). Its applicability was demonstrated by deriving the optimized shape of the model free-form building for various climate zones. Established models that returned the thermal characteristics of a free-form building were used as objective functions, which are critical in the GA optimization process. The results showed that the proposed process could rapidly predict and optimize the variation of the heat gain and loss characteristics that was caused by changing the building shape.
The purpose of this paper is to suggest a simple way of determining the thermal characteristic of free-form buildings, which can be applied at an early stage of the design phase. The thermal load of a reference building estimated using TRNSYS 16 is compared with those for various free-form buildings that have the same envelope area as the reference building. A database of thermal load ratios (i.e. the ratio of the free-form building's thermal load over the reference building's thermal load) was generated. Then, polynomial equations returning the envelope heat gain, heat loss, and solar heat gain ratios were derived as a function of window area ratio and exterior wall area ratio of a free-form building. It was found that the proposed models are able to predict the heat gain, heat loss, and solar heat gain characteristics of a free-form building within 10% of discrepancy, compared with detailed thermal load simulation results.
An application of a novel steady-state simulation of a monovalent inverter-driven water-to-water heat pump with a desuperheater is presented. The simulation is capable of modeling and managing the different operation modes of the system such as combined space heating and domestic hot water, in order to provide realistic results, from which four control schemes based on two electricity tariff plans were developed. The schemes were tested on a subject low energy house for Tokyo and Sapporo, in Japan, for the coldest day of the year and for the heating season. Emphasis was given to the short-term domestic hot water storage. The results show that the application of the schemes with a flexible electricity tariff had lower cost over the scheme that used the flat rate pricing plan. Moreover, it was shown that no unique scheme was able to minimize the cost and electrical consumption for both locations. Additionally, a tradeoff between lowest cost and lowest energy consumption was identified for Sapporo. The results of this research could be applied to develop a more robust control on monovalent heat pump systems.
Free-cooling by ventilation is one of the most energy efficient techniques for cooling. When ventilation is used for cooling, variable airflow rates should to be used in order to achieve comfortable room temperatures and to minimize the energy demand for mechanical ventilation. Thus, free-cooling, requires, obviously, the existence of a potential for cooling and needs control mechanisms for the airflow. In this study, the free cooling potential by ventilation for office buildings is evaluated by the free-running temperature. The free-running temperature approach is based on the energy balance of heat gains and losses. It is adapted to evaluate the potential for free cooling by ventilation of office buildings for which the gains through the walls are negligible as compared to the internal and solar gains. The free-running temperature of each office room considers solar and internal heat gains, outdoor temperature, indoor temperatures and ventilation air flow rates. The approach is applied to 14 office rooms in a passively cooled office building in Germany and is used to estimate the potential and to evaluate the total energy saving by free cooling by ventilation. The good fit between monitoring data and calculation procedure proves that the free cooling potential can be accurately estimated by using the difference between the comfort limits, i.e. the target value of the indoor temperature, and the free-running temperature.
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.
The choice of the design solutions for low energy buildings requires the performance evaluation of the alternatives. In the present study, simple polynomial function is used to estimate the annual energy demand as a function of building envelope physical and geometric parameters. The coefficients of this function are obtained by regression from dynamic simulation results. The number of dynamic simulations needed is reduced by using the design of experiments method. The model shows a good agreement with the dynamic simulation results.
The design of a building should provide the flow paths needed for natural ventilation. Therefore, the decision to apply natural ventilation should be taken early in the building design process, when little information is available for airflow estimation. To deal with this lack of data, a semi-qualitative method to assess the potential of an urban site to host a naturally ventilated building is proposed. First, natural ventilation driving forces and constraints are assessed by using comfort criteria, statistical meteorological data and userprovided information. Then, the site of interest is compared to other, well known sites using criteria related to both natural ventilation driving forces and constraints. This method compares and ranks the site within the base sites using a qualitative multicriteria analysis procedure. The result of the comparison shows if the assessed site has a higher potential for application of natural ventilation than a set of known sites.
An algorithm is presented in this paper for computing state-space balancing transformations directly from a state-space realization. The algorithm requires no "squaring up" or unnecessary matrix products. Various algorithmic aspects are discussed in detail. A key feature of the algorithm is the determination of a contragredient transformation through computing the singular value decomposition of a certain product of matrices without explicitly forming the product. Other contragredient transformation applications are also described. It is further shown that a similar approach may be taken, involving the generalized singular value decomposition, to the classical simultaneous diagonalization problem. These SVD-based simultaneous diagonalization algorithms provide a computational alternative to existing methods for solving certain classes of symmetric positive definite generalized eigenvalue problems.
New Dutch office buildings are built under strict energy efficient legislation and are well equipped with insulation, condensing boilers, heat recovery systems, and so on. Despite this, many buildings do not have good energy efficiency and also do not meet comfort requirements. This has led to a large number of complaints. The problem seems to be that ‘common’ energy control strategies (heating/ cooling curves) are often used, most of the curves were based on buildings with low levels of building shell heating resistance, no additional insulation and single glazing. When these control strategies are used in modern office buildings, too much energy is used due to a mismatch between heating and cooling demands. Energy savings of up to 35% can be realized without significant financial investments by devoting extra attention to the settings of the heating/ cooling curve in the central air handling units (AHUs) of HVAC systems without loss of comfort. In most cases the number of complaints will also be reduced. The design method can be used in both new and existing buildings. The first part of this paper presents a brief description of the method used to realize this energy saving. The brief description is followed with a practical example of this method. The intention is that the approach will generate a building-specific heating/cooling curve based on an energetic optimum between the demand for heating and cooling in the zones. The input data used to generate the function cover the characteristics of the building, the organization, and the HVAC equipment.
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.
At the new institute building of Fraunhofer ISE, both mechanical and free night ventilation is used for passive cooling of the offices. The results from a long-term monitoring show, that room temperatures are comfortable even at high ambient air temperatures. In two offices, experiments were carried out in order to determine the efficiency of night ventilation dependent on air change rate, solar and internal heat gains. The aim is to identify characteristic building parameters and to determine the night ventilation effect with these parameters. The experiments (one room with and one without night ventilation) are evaluated by using both a parametric model and the ESP-r building simulation programme. Both models are merged in order to develop a method for data evaluation in office buildings with night ventilation and to provide a simple model for integration in a building management system.
Natural ventilation is one of the most effective techniques for cooling. Its potential for cooling may be assessed by using a method based on the indoor–outdoor temperature difference of the free-running building, the adaptive comfort criteria and the outdoor temperature. It is demonstrated that the free-running temperature may be used instead of the balance temperature in energy estimation methods. The indoor–outdoor temperature difference of the free-running building becomes a characteristic of the thermal behavior of the building which is decoupled from comfort range and outdoor temperature. A measure related to the energy saved and the applicability of free-cooling is given by the probabilistic distribution of the degree-hours as a function of the outdoor temperature and time. Weather data for this method are available in public domain from satellite investigation. The method can be applied when buildings similar to existing ones are constructed in a new location, when existing buildings are retrofitted or when completely new buildings are designed. The method may be used to interpret the results of building simulation software or of the field measurements.
This paper presents the experimental results and specific thermal and energetic saving analysis from the systematic monitoring carried out to analyse the energetic performance of a building with an innovative component, which is based on an optimisation of the sawtooth roof concept.
This component has been installed at a building of the University of Almería. The constructive goal has been to refurbish an existing 350 m2 courtyard, without practical utilisation before that moment, so as to incorporate energy saving techniques in the climatic conditioning of the available resulting zone. The innovative elements of the modified sawtooth roof are South orientated openings, thermal insulation, overhangs and integrated devices to improve natural ventilation as solar chimneys into interior space.
Equipment, thermal and meteorological sensors have been installed, and the most representative parameters to analyse the thermal performance of the space have been recorded for a year. These data have made possible to analyse the feasibility, regarding functionality and thermal response of the constructive solution adopted. The analysis has been carried out through the climatic description and comparison with the climate measurements for the recorded monitoring period, and afterwards the most important efforts have been done for the thermal analysis that included the thermal comfort and the energetic characterisation of the building using the monitored data measurements.
Decisions taken in the early stages of architectural design have an important impact on energy demand and efficiency. In this design phase, architects and engineers need to manipulate common concepts that reveal the influence of building, comfort criteria, and climate on energy consumption. Such a concept is the indoor temperature of the free-running building, which combined with comfort criteria and climate, allows to assess the relative weight of heating, ventilation and air conditioning systems in the energy budget. Based on this concept and on the probabilistic distribution of the outdoor air temperature, a method was developed that indicates the heating and cooling needs, and gives a measure of the energy saved for cooling when ventilation is used. This method also gives the load for which the mechanical systems should be designed to obtain maximum efficiency and shows the feasibility of stack-induced natural ventilation. Weather data needed for this method are available in public domain from satellite investigation. The method can be applied when buildings similar to the existing ones are constructed in a new location, when existing buildings are retrofitted or when completely new buildings are designed.
This paper inquires into whether overall thermal transfer value (OTTV) is an appropriate building envelope energy performance index for use in regulatory control. First, a historical review of the use of OTTV in American Society of Heating, Refrigerating and Air-conditioning Engineers (ASHRAE) Standard 90 is presented, followed by a review of more recent work on its further development and application. The major deficiencies of OTTV are then discussed, and simulation study results meant to highlight the impacts of such deficiencies are presented. The study embraced air-conditioned office buildings and air-conditioned high-rise residential buildings in Hong Kong. Results of this study clearly show that the OTTV calculated with the use of pre-calculated coefficients may not truly reflect the thermal performance of a building envelope. Therefore, a second thought should be given to the use of OTTV in building energy codes.
Architects form an integral link in the design of efficient buildings. Energy-efficient design strategies therefore, require architects and engineers to work closely together in optimising the building shell. However, this is not always practical. Architects must therefore, be able to perform a preliminary thermal analysis if energy efficient design strategies are to succeed. Existing tools do not cater for them or fit their design methodology. A need therefore exists for a simplified thermal design tool for architects. This article discusses the development of a tool to fulfil this requirement.
We calculated the influence of thermal mass and night ventilation on the maximum indoor temperature in summer. The results for different locations in the hot humid climate of Israel are presented and analyzed. The maximum indoor temperature depends linearly on the temperature difference between day and night at the site. The fit can be applied as a tool to predict from the temperature swing of the location the maximum indoor temperature decrease due to the thermal mass and night ventilation. Consequently, the fit can be implemented as a simple design tool to present the reduction in indoor temperature due to the amount of the thermal mass and the rate of night ventilation, without using an hourly simulation model. Moreover, this design tool is able to provide for the designer in the early design stages the conditions when night ventilation and thermal mass are effective as passive cooling design strategy.
This study focuses on the impact of the meteorological environment and the building's thermal inertia on the indoor temperature performance in a passively cooled office building with night ventilation. Data from a long-term monitoring campaign and short-term measurements are used to identify the correlation between outdoor and indoor temperature in a building under ordinary use and to derive the energy balance of an office room. Temperature performance and energy balance are merged in a comprehensive thermodynamic model, which takes steady-state conditions, short-term and long-term dynamics into account. In contrast to extensive simulation studies, the presented method is based on a straightforward mathematical model using Fourier analysis of temperature time series and an energy balance model, which focuses on fundamental information.
Kalman's minimal realization theory involves geometric objects (controllable, unobservable subspaces) which are subject to structural instability. Specifically, arbitrarily small perturbations in a model may cause a change in the dimensions of the associated subspaces. This situation is manifested in computational difficulties which arise in attempts to apply textbook algorithms for computing a minimal realization. Structural instability associated with geometric theories is not unique to control; it arises in the theory of linear equations as well. In this setting, the computational problems have been studied for decades and excellent tools have been developed for coping with the situation. One of the main goals of this paper is to call attention to principal component analysis (Hotelling, 1933), and an algorithm (Golub and Reinsch, 1970) for computing the singular value decompositon of a matrix. Together they form a powerful tool for coping with structural instability in dynamic systems. As developed in this paper, principal component analysis is a technique for analyzing signals. (Singular value decomposition provides the computational machinery.) For this reason, Kalman's minimal realization theory is recast in terms of responses to injected signals. Application of the signal analysis to controllability and observability leads to a coordinate system in which the "internally balanced" model has special properties. For asymptotically stable systems, this yields working approximations of X_{c}, X_{bar{o}} , the controllable and unobservable subspaces. It is proposed that a natural first step in model reduction is to apply the mechanics of minimal realization using these working subspaces.
Estimating and Modeling Methods, in ASHRAE Fundamentals
- Energy Ashrae
ASHRAE, Energy Estimating and Modeling Methods, in ASHRAE Fundamentals. 2001.