Search

[Display omitted] • A split-degree days index is proposed for building energy use/climate classification. • Split-degree days index is shown to better estimate solar radiation and humidity data. • The estimation of building energy end-uses is improved using the split-degree days. • Split-degree days also showed improved results for high thermal mass and 24-hr models. • Split-degree days index maintains a high accuracy for varying base temperatures. Degree days have been widely used in different applications in buildings, such as estimating building energy use and climate classification for building energy standards. However, there are limitations with the use of conventional degree days that result in inaccuracies in estimating building energy consumption using degree day-based methods. This paper proposes a new method, the split-degree day method, that shows substantially improved results in the accuracy of the building energy use estimation compared to the conventional degree day methods. The analysis in 801 locations in the U.S. using regression models showed that the new, proposed split-degree day method in this paper, compared to the conventional degree day method, better accounts for the weather parameters and more accurately estimates end-uses. The split-degree days showed an improvement of over 5% in the accuracy of the total annual energy use prediction, 8% for predicting the heating energy use, 0.3% for predicting the cooling energy use, and 33% for predicting the fan energy use. Additionally, the analysis showed improved results for the model with higher thermal mass and the model with a 24-hour operating schedule. These improvements were due to the fact that the split-degree day method includes more information related to the weather characteristics of a location, which is missing in a conventional degree day calculation procedure that relies on aggregated data at daily intervals. Furthermore, as opposed to the conventional degree days, the accuracy of the estimations using the split-degree days are shown to maintain their high accuracy when the base temperature deviates from the optimal base temperature. The proposed method can be used as a more accurate alternative to the conventional degree days in different applications, including but not limited to building climate classification, building energy estimation, and weather normalization for building energy savings calculation. [ABSTRACT FROM AUTHOR]

While IEA EBC Annexes usually are focused on R&D about single energy technologies, their broad scale use in practice is of even higher importance to contribute to global energy goals. This practical application of low-carbon technologies on community scale has been the subject of Annex 51, carried out from 2010 to 2013. In this Annex, evaluating 25 community energy case studies of the 10 participating countries, a ‘Guidebook on Successful Urban Energy Planning’ has been extracted from these evaluations to directly address practical urban/energy planners and municipal decision makers. The main content of the Guidebook with respect to planning approaches is summarized in this paper, distinguishing between the different planning fields of ‘urban energy planning’, which is addressing whole cities and communities, and ‘local energy planning’, oriented to neighbourhoods or building clusters. The necessary link between both is emphasized, considering a city as an interconnected energy system requiring a holistic approach rather than a compartmented consideration. As a side effect, a general method to calculate the primary energy performance of buildings and neighbourhoods employing different energy systems, including energy efficiency or energy conservation technologies, was derived. Using appropriate system characteristics, such as seasonal performances, this approach can be applied for every energy system, and for every building or neighbourhood. It is presented in this paper supplying an example from one of the case studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • 33 worldwide open datasets of city-level building energy consumption are summarized. • Representative applications of 198 papers using the collected datasets are analyzed and compared. • Policy implications are presented for using the open datasets to support policymaking. • Useful non-energy datasets about weather, building features, urban forms, and occupant-related factors are recommended. • Solutions to privacy issues are explored, including anonymizing methods and proper data aggregation. Data related to building energy use fuels the research and applications on building energy efficiency, which is an essential measure to address global energy and environmental challenges. However, in most cities, there is a lack of comprehensive and publicly accessible building energy use datasets with necessary temporal and spatial granularities to support urban building energy modeling, regional energy planning, city-level building performance benchmarking, and policymaking on building energy efficiency. Data owners and governments are facing challenges in determining which and how building energy use data should be disclosed at the city level and how to protect data privacy. This review paper provides insights to answer these questions based on a comprehensive and critical review of worldwide open datasets and their applications in the built environment context. Detailed information about the collected 33 building energy datasets is summarized and categorized. Studies identified into 11 subdomains using these open datasets are critically analyzed and compared. Potential policy implications based on the studies are also proposed. Moreover, non-energy datasets that are frequently used in research relevant to urban building energy use are also introduced. Solutions to privacy issues are discussed to address concerns from data publishers. Finally, significant conclusions are made to support the proper disclosure of city-level building energy data. This review study is valuable to urban building energy data disclosure, urban building energy modeling, and data-driven energy policymaking. [ABSTRACT FROM AUTHOR]

• A BIM-supported energy performance analysis for green building design is proposed. • This systematic analytical framework integrates explainable machine learning and multi-objective optimization. • It can serve as a data-driven decision tool to guide green building development even under uncertainty. • The top important features that greatly affect the building energy performance can be quantitatively identified. • Building energy performance can be improved by 13.43% using the obtained optimal strategy. Supported by the combination of the advanced BIM technique with intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization to realize the automatic prediction and optimization of building energy performance towards the sustainable development goal. There are three critical parts incorporated, including the DesignBuilder simulation, BO-LGBM (Bayesian optimization-LightGBM) and an explainable method SHAP (SHapley Additive explanation)-based prediction and explanation of building energy performance, and AGE-MOEA algorithm-based multi-objective optimization (MOO) under sources of uncertainty. It has been verified in a case study for green building design. Results show that: (1) The predictive BO-LGBM model can make a highly precise prediction in agreement with the simulation data, reaching up the R2 larger than 93.4% and MAPE smaller than 2.13%. From the SHAP calculation, features related to the HAVC (Heating Ventilation and Air Conditioning) system tend to contribute more to affecting the prediction results. (2) The AGE-MOEA-based optimization can identify a set of Pareto optimal solutions in simultaneously minimizing the building energy consumption, CO 2 emission, and indoor thermal discomfort degree, arriving at the highest optimization rate of 13.43% under proper adjustment of certain features. (3) In the MOO task, the consideration of model and data uncertainty by prediction intervals and Monte-Carlo simulation can further increase the optimization rate by around 4.0% than the deterministic scenario, resulting in more desired strategies in optimizing the green building performance. In short, this paper enriches the area of green building development. For one thing, it raises the transparency and interpretability of machine learning to make the prediction more convincing. For another, it efficiently determines the passive and active design solutions along with the detailed profile of influential factors for green building design. [ABSTRACT FROM AUTHOR]

This paper explores the effects of residential neighborhood types on building energy consumption and solar energy trade-offs with the goal of developing an operational methodology to achieve the best conditions towards a zero nonrenewable energy neighborhood. Six typical Chongming neighborhood types are identified, termed the Farmhouse, Linear-village, Nucleated-village, Townhouse, Slab and High-rise types with the effects of increased installation of solar energy panels estimated by Monte Carlo simulation. To achieve a more comprehensive view of cost-effective opportunities to improve the performance of buildings at the neighborhood scale, we use actual energy use data obtained from both the survey and electricity bills’ analysis to show that considerable variations exist due to hitherto unrecorded socioeconomic factors relating to the type of occupants involved. By concluding that the associated variables are interdependent, the paper continues with an empirical study in which the solar energy potential of each neighborhood is quantified to show that, in the case of Chongming, the Nucleated-village and Slab neighborhood types have the greatest retrofitting potential. These investigations into energy trade-offs with existing retrofitting and construction technologies therefore provide a metric for other eco-cities to use as a tool for future policy guidance. [ABSTRACT FROM AUTHOR]

Research has shown the large effect that occupants have on buildings' performance. Uncertainties related to the actual energy consumption of buildings increase the risks for the investments in low carbon technologies. Monitoring building occupancy can potentially decrease these uncertainties by providing more information about the occupants and their behaviour. The objective of the investigation is to establish an approach to inform the design process (e.g. building simulation) by addressing the complexity of occupants behaviour. The approach integrates information on occupants' behaviour and attitudes regarding energy use and indoor conditions to determine the requirements for building simulation and energy calculations. This paper presents the results of two monitoring campaigns in which the approach was employed. The monitoring campaigns focused on two owner-inhabited apartments in Spain and three social rental dwellings in The Netherlands. The results have given first insights of the power of the methodology to obtain detailed and understandable data on the occupancy patterns. This investigation highlighted the importance socio-economical status and attitudes towards energy conservation on occupants' behaviour in residential buildings. The methods described in this paper can be readily used to develop occupancy and heating profiles for monitored households to be used in building simulation programs. [ABSTRACT FROM AUTHOR]

Occupants account for a significant impact on building performance through their interactions with zone level building components. Empirically-derived occupant models, despite their potential to represent occupants’ impact in building performance simulation (BPS), have minimal penetration in design and code compliance processes. Instead, occupants are represented with static schedules or simple deterministic triggers. The objective of this paper is to better understand the influence of assumptions made in representing occupant interactions with building components over a BPS model's energy use and comfort predictions, as well as their ability to promote better design decisions. To this end, the energy and daylight performance of a generic perimeter office space in Ottawa, Canada were evaluated using a set of comprehensive performance metrics. Results indicate that representing dynamic occupant–building interactions lead to different energy predictions from the static schedules. The maximum difference in the total electricity use was for window-to-wall area ratios (WWR) of 20%, which was about 30% higher with the stochastic cases than the blind-open static cases. WWR 60% and 40% generally yielded the lowest lighting electricity use with the static and stochastic cases, respectively. This paper emphasizes the importance of incorporating empirically-derived dynamic occupant models for simulation-aided design and code compliance. [ABSTRACT FROM AUTHOR]

In a hybrid cooling system, an indirect evaporative cooling (IEC) unit is installed before an air handling unit to pre-cool the incoming fresh air, in order to reduce the energy consumption of the air-conditioning system. When the system is applied in humid regions, as the dew point temperature of the fresh air is higher, condensation will probably occur in the dry channels of IEC unit. However, the model of IEC unit considering condensation had been seldom studied, which would become an obstacle in predicting the performance of the hybrid system. In order to evaluate its energy saving potential precisely, the paper aims to establish a short-cut model for predicting the annual performance of the hybrid cooling system considering condensation in IEC unit. This paper firstly presents the analytical models for IEC under non-condensation, totally condensation and partially condensation states and proposed a method for judging the three states as well. The models were validated by the published data from literatures. The validated model was then incorporated with TRNSYS for predicting the performance of an IEC hybrid cooling system in a wet market in Hong Kong and the annual simulation results were validated by a field measurement with good agreement. [ABSTRACT FROM AUTHOR]

The increase in global environmental concerns as well as the advancement of computational tools and methods have had significant impacts on the way in which buildings are being designed. Building professionals are increasingly expected to improve energy performance of their design. To achieve a high level of energy performance, multidisciplinary simulation-based optimization can be utilized to help designers in exploring more design alternatives and making informed decisions. Because of the high complexity in setting up a building model for multi-objective design optimization, there is a great demand of utilizing and integrating the advanced modeling and simulation technologies, including BIM, parametric modeling, cloud-based simulation, and optimization algorithms, as well as a new user interface that facilitates the setup of building parameters (decision variables) and performance fitness functions (design objectives) for automatically generating, evaluating, and optimizing multiple design options. This paper presents an integrated framework for building information modeling (BIM)-based performance optimization, BPOpt. This framework enables designers to explore design alternatives using an open-source, visual programming user interface on the top of a widely used BIM platform, to generate models of building design options, assess the environmental performance of the models through cloud-based simulation, and search for the most appropriate design alternatives. This paper details the process of the development of BPOpt and also provides a case study to show its application. The case study demonstrates the use of BPOpt in minimizing the energy consumption while maximizing the appropriate daylighting level for a residential building. Finally, strengths, limitations, current adoption by academia and industry, and future improvements of BPOpt for high-performance building design are discussed. [ABSTRACT FROM AUTHOR]

There is a trend in the world to adopt low or zero carbon building design principles and technologies. However, energy performance gap is a significant barrier to the development of low or zero carbon buildings. A predominant reason of the performance gap is the insufficient consideration of residents’ needs, which may lead to subsequent inappropriate user behaviours. Previous research on energy-related occupant behaviour is built either on statistical analysis from post-occupancy evaluations or stochastic models. There is a lack of pre-occupancy measurement of the interaction between occupant behaviour and building design alternatives. This paper aims to develop an innovative analytic approach using virtual reality (VR) technology to help building designers (architects, system engineers and interior designers) to identify the design pattern which guides the occupants to behave in the most energy-efficient way, so as to closing the energy performance gap resulting from occupants’ misconduct. The paper presents a VR-assisted innovative occupant-engaged framework in building energy design based on Design with Intent (DwI) method. Then the approach is validated by a case study on lighting control design for a sample residential building project in Hong Kong. The combination with decision-making is recommended for further research. [ABSTRACT FROM AUTHOR]

Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance. [ABSTRACT FROM AUTHOR]

As the scope of building design and construction increases and building systems become more integrated, the use of building energy models has become increasingly widespread in evaluating and predicting building performance. Despite the growing sophistication of building modeling tools, errors can arise from approximations that are made by a practitioner during model creation. This paper examines the process of model zoning, i.e., how the volume of a building is divided into regions where properties are assumed to be uniform. Zoning is performed during model creation to decrease model complexity. However, accuracy reduces when dissimilar regions of a building are defined by a single zone. In this paper, a systematic approach to creating zoning approximations is introduced. Utilizing the Koopman operator, the time-series output produced by a building simulation can be decomposed into spatial modes which capture the thermal behavior of a building at different time-scales. Identification of spatial structures within these modes forms a framework for the creation of simplified models of varying levels of granularity. In this paper, a detailed model is analyzed, and model accuracy is studied as coarser building representations are created using the introduced method. [ABSTRACT FROM AUTHOR]

Among the various factors that affect building performance, occupant behavior (OB) has received more and more attention and has become a research hotspot in building design and operation in recent years. However, due to the remarkable randomness and diversity of OB in buildings, it is difficult to accurately describe its characteristics using a fixed schedule, which is a normal approach of most energy simulation tools. DeST, a whole building energy simulation tool offers the function of stochastic OB simulation so that the simulation results can be more in line with the actual situation. In the newly released DeST 3.0, the OB modules are developed based on the actual observed OB data and are divided into two parts: occupancy and occupant action modules. The occupancy module is based on the Markov chain, which simulates the occupant movements among the spaces inside and outside the buildings. The occupant action modules are proposed with an environment and event-driven stochastic infrastructure based on a three-parameter Weibull distribution, such as occupant shading, window, and air-conditioning (AC)/heating control behavior modules. This paper presents the OB modules development for coupled simulation in DeST 3.0 and discusses the effects of integrated simulation of occupant behavior on building energy consumption. Firstly, an overview of the current modelling efforts of OB is given, from the trend to the existing foundation. Secondly, the development of OB modules for coupled simulation in DeST 3.0 is introduced in-depth, including co-simulation of AC/heating control behavior, window behavior and shading behavior. Lastly, two case studies that realize coupled simulation using the developed modules are shown, one is the comparison between existing occupancy schedules from GB standards and the developed occupancy simulation, and the other is the comparison between the result of simulating with or without using AC/heating control behavior modules in two bedrooms of the residential building. [ABSTRACT FROM AUTHOR]

Highlights • The aim of this research is to investigate the thermal performance of the prototype building to assess overheating in the summer, particularly the long-term heatwave period in the UK. • To understand the impact of energy efficient technologies on occupants' wellbeing and health in order to identify occupants' thermal comfort level in these types of dwellings. • The study adopts a quantitative research design based on indoor monitoring, in-situ measurements and dynamic simulation modelling. • This clearly indicates that due to this prototype house's structural construction material behaviour in a heatwave and its location (southwest facing orientation), it is susceptible to very high overheating risk. • The results found from the monitoring during a long-term heatwave in the summer of 2018 provided strong evidence for overheating and thereby thermal discomfort in many occupied spaces, particularly on the first floor followed by several spaces on the ground floor. Meanwhile, all the occupied spaces appeared to be well above the acceptable thermal comfort benchmark throughout the data collection period. • To calibrate the building performance, as well as the building materials and components adopted in the model, the measured indoor environment data were scrutinised in conjunction with the IES simulation results. Abstract There is growing evidence that terraced houses—thermally lightweight, well insulated, naturally ventilated with three exposed wall surfaces—are at risk of overheating, especially in south-eastern England. The aim of this study is to evaluate the building performance and develop a reliable building simulation, which will be employed in the second phase of the study: developing affordable and feasible passive design strategies to support the energy-efficient building systems of the construction industry. This paper reports on the results from the first phase of the study where a quantitative methodology, including indoor and outdoor environmental monitoring, in-situ measurements and building simulation modelling, was adopted. The performance of a case study was modelled and simulated via employing Integrated Environmental Solutions (IES) software suite. The results from the base-case were analysed according to the adaptive thermal comfort of Chartered Institution of Building Services Engineers (CIBSE) Technical Memorandum 52 guidelines: The Limits of Thermal Comfort—Avoiding Overheating in European Buildings. The spaces studied within the case study house were observed to exceed the acceptable limits of thermal comfort; particularly, the large bedroom within this zone exceeded the upper limit for overheating up to 11 h daily. Furthermore, the results from the monitoring study indicate a high risk of summertime overheating across all the case study settings, especially during short-term peaks in outdoor temperatures. The main reasons for the problematic thermal performance were identified as well-insulated and fully air-tight building fabric, the lack of sufficient ventilation through the living spaces and excessive heat gains through the composite cladding material. [ABSTRACT FROM AUTHOR]

Highlights • A methodology is introduced to rank a set of buildings based on energetic performance of occupants. • Ranking is done in two levels, each one gives specific information about occupants of a single building. • The underlying factors that create the ranks are examined to give occupants clues on how to improve their rank and reduce energy consumption. Abstract Identifying the impacts of occupants on building energy consumption has become an important issue in recent years. This is due to the interrelationship of influencing factors such as urban climate, building characteristics, occupant behavior, and building services and operation, which makes it challenging to identify the role of occupants in energy consumption. The research problem in this study lies in the fact that the occupants of a building may not be cautious regarding energy savings, and there exists no ground to assess their energy consumption behavior. One solution is the development of a systematic comparison procedure between similar buildings. This paper introduces a new procedure for comparison between occupants of several buildings to show the rank of each building among others and suggest occupants on reducing their energy consumption and improving their rank. The proposed framework is developed based on multiple data-mining methods, including clustering, association rules mining, and neural networks. The proposed methodology is composed of two levels. The first considers the amount of energy usage by occupants after filtering effects unrelated to the occupant behavior. The second ranks the buildings in terms of achieved and potential savings during the time under investigation. To demonstrate the application, the methodology was applied on a set of monitored residential buildings in Japan. Results suggest that the proposed method enhances the evaluation of buildings' energy-saving potential by revealing the occupants' contribution. It also provides diverse and prioritized strategies to help occupants manage their energy consumption by revealing the building energy end-use patterns. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Highlights • Create city-specific energy performance grading model using city disclosure data. • Method to grade buildings that is interpretable, reproducible, scalable, and robust. • Develop a clustering algorithm to grade buildings on performance. • Validated approach demonstrates significant improvements over current methods. Abstract As the effects of anthropogenic climate change become more pronounced, local and federal governments are turning towards more aggressive policies to reduce energy use in existing buildings, a major global contributor of carbon emissions. Recently, several cities have enacted laws mandating owners of large buildings to publicly display an energy efficiency rating for their properties. While such transparency is necessary for market-driven energy reduction policies, the reliance on public-facing energy efficiency grades raises non-trivial questions about the robustness and reliability of methods used to measure and benchmark the energy performance of existing buildings. In this paper, we develop a building energy performance grading methodology using machine learning and city-specific energy use and building data. Leveraging the growing availability of data from city energy disclosure ordinances, we develop the GREEN grading system: a framework to facilitate more accurate, fair, and contextualized building energy benchmarks that account for variations in the expected and actual performance of individual buildings. When applied to approximately 7500 residential properties in New York City, our approach accounts for the differential impact of design, occupancy, use, and systems on energy performance, out-performing existing state-of-the-art methods. Our model and findings reinforce the need for more robust, localized approaches to building energy performance grading that can serve as the basis for data-driven urban energy efficiency and carbon reeduction policies. [ABSTRACT FROM AUTHOR]

Building energy performance in existing stocks via facilities management interventions for low carbon building has become expedient and relevant in global climate change discourse. It has raised the consciousness for the need for a unified decision-support model for Facilities Managers and Owners, which could be used for office buildings across countries in achieving cleaner building energy production and use. This paper aimed at examining the factors that affect office Building Energy Performance; their interdependencies; and identify the critical path for interventions. It filled this gap by presenting a combination of interrelated processes (operations, tactics, and strategies) needed to improve building energy performance, reduce costs and greenhouse gas emissions in buildings for organizations. An online questionnaire survey was used in gathering data on current study model variables from participants of case study office buildings in Nigeria and the UK. Structural Equation Modelling technique was used to examine the factors that contribute to improving the energy performance of heterogeneous office buildings in both countries. The result established a strong correlation among observed variables and constructs and high covariance between constructs. This indicates that dependency and interdependence relationships exist amongst constructs, and in between construct and indicators. The finding reveals that an organization needs Sustainability Policy, Facilities Management and Energy Management as a sub-set of Strategic policy incorporated into its core management policy and operations energy management to achieve low carbon building. It also reveals the most critical pathway in the overall model with Strategic Facilities Management discovered to underpin the optimal performance for office buildings. [ABSTRACT FROM AUTHOR]

Highlights • This study presents a detailed review on IRT for the investigation of building envelope defects. • The literature survey is conducted regarding existing IRT methodologies for building envelopes. • The previous studies with measurement methods, analysis schemes and analysis types are categorized in the literature matrix. • Recent advances and future trends on IRT methods are presented. Abstract Thermal radiation generated by limited resources is emitted from the surfaces and interpreted as temperature. On the other hand, the limited resources necessitate prevention of energy leakages causing noticeable temperature differences on surfaces. Providing sensitive information on temperature contrast, infrared thermography (IRT) is a prominent tool for detecting infrared energy on the surfaces of the objects. IRT is also very promising method for detecting thermal irregularities, air leakages and even moisture abnormalities on building envelopes. Since the concept of nearly zero energy buildings is to be the main target of energy policies in European Union published by European Commission, improving thermal performance of buildings becomes crucial in the last decade. Therefore, implementation of IRT for visualizing and analyzing concealed defects on building envelopes is very promising for improving the energy efficiency of buildings. Given recent regulations and benefits of emerging thermogram technologies mentioned above, a detailed review of the previous studies investigating the abnormalities of building envelopes by IRT is presented in this paper. Furthermore, the applications with measurement methods, analysis schemes and analysis types are classified to highlight the potential of IRT in inspection of building envelopes for providing energy efficient solutions. Finally, recent advances and practical future opportunities are presented for additional contribution of the IRT methods previously employed. [ABSTRACT FROM AUTHOR]

Abstract The actual thermal behaviour of façades is important to identify suitable energy-saving measures and increase the energy performance of existing buildings. However, the accuracy of in situ measurements of façades’ U-values varies widely, mostly due to inadequate test durations. The aim of this paper was to evaluate the minimum duration of in situ experimental campaigns to measure the thermal transmittance of existing buildings’ façades using the heat flow meter method, and to analyse the thermal performance of the façade during the test. Minimum test duration was determined according to data quality criteria, variability of results criteria, and standardized criteria for different ranges of theoretical thermal transmittance and for the same range of average temperature difference. Then, the minimum test duration results were compared. The findings show that ISO criteria are more sensitive and provide more accurate results, requiring a longer test duration. However, when certification is not required, the duration of the test could be reduced by applying data quality and variability of results criteria. The minimum duration of experimental campaigns depends on the theoretical thermal transmittance and the stability of climatic conditions. Moreover, results are more accurate when the dynamic method is used. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy worldwide in maintaining comfort for occupants. Building energy management systems (BEMS) are employed to ensure that the energy consumed is used efficiently. However these systems often do not adequately perform in minimising energy use. This is due to a number of reasons, including poor configuration or a lack of information such as being able to anticipate changes in weather conditions. We are now at the stage that building behaviour can be simulated, whereby computer programs can be used to predict building conditions, and therefore enable buildings to use energy more efficiently, when integrated with BEMS (i.e. simulation assisted control [1]). In this paper we demonstrate a low cost BEMS that uses building simulation to predict optimised electrical heating startup control points. Those that use electricity for heating in Scotland, where this study was based, tend to be fuel-poor, hence there is a strong case for optimisation, particularly when electricity costs nearly three times as much as the equivalent unit of gas for heating applications. The proposed system demonstrated a 50% energy saving in the reduced heating time compared to scheduling when retrospectively evaluated. [ABSTRACT FROM AUTHOR]

A pronounced gap often exists between expected and actual building performance. The multi-faceted and cross lifecycle causes of this performance gap are found in design assumptions, construction issues and commissioning and operational compromises. Some important factors are firmly rooted in the lack of interoperability around building information. New solutions to the interoperability challenge offer the potential to leverage and reuse available heterogeneous data in a manner that can significantly assist building performance assessment. Linked data provides an open, modular and extensible solution for the challenge. However, in the buildings domain, the integration of rule-based performance metrics and contextual information has yet to be formally established. This paper describes an approach to the provision of in-depth building performance assessment through the integration of OpenMath and linked data. An ontology describing performance metrics in RDF is presented, together with an automated metric evaluation solution using multi-silo queries and computer algebra systems, providing a flexible, automated and extensible mechanism for the assessment of building performance. Building managers and engineers can simultaneously analyse time-series building performance at a range of levels, without burdensome manual intervention such as is the case with traditional solutions. A test implementation on a large university building highlights the potential of this solution. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the influence of building fabric, services and occupant related factors on actual energy use of six case study dwellings, located in three new low energy social housing developments in UK, covering a variety of built forms and construction systems (timber frame, hempcrete, steel-frame). Physical monitoring of indoor environment and window-opening is cross-related with building fabric and systems’ performance, and qualitative data gathered through occupant surveys, review of control interfaces and handover guidance, to understand the causes of the gap between modelled and measured energy use. Actual energy use is found to exceed design expectations by a factor of three, questioning the need for whole-house mechanical ventilation heat recovery (MVHR) systems at measured air permeability rates of 6 m³/(h.m²) against the design target of 3 m³/(h.m²). Lack of proper commissioning of MVHR and heating systems, combined with inadequate user comprehension about their operation and control leads to occupant ‘misuse’ wherein systems are de-activated, thereby negatively affecting indoor air quality. This is confounded by occupant factors related to higher demand temperatures, unexpected opening of windows during winters due to under-performance of MVHR combined with habitual behaviours, and over-use of heating systems to compensate for higher than expected air permeability. [ABSTRACT FROM AUTHOR]

This study investigates an energy-inefficient council housing tower block in London, due for retrofit, by exploring the correlations between occupancy and energy consumption patterns, thermal comfort and building energy performance in the winter. The aim of this study is to evaluate the building performance and develop a reliable building simulation model to be employed in the second phase of the study for developing an energy-efficient and cost-effective retrofit strategy to support the council's plans. The research seeks to demonstrate the significance of using dominant occupancy and energy use profiles as opposed to relying on standardised profiles when calculating building energy consumption using building simulation software. The paper reports on the results from the first phase of the study where a quantitative methodology; including a questionnaire, structured interviews, indoor monitoring, and building simulation modelling was adopted. The results provide evidence that occupants’ socio-demographic characteristics have a considerable impact on household energy consumption and fuel bills. The results also show that indoor environmental issues experienced in many flats are partly attributed to the thermally inefficient building envelope and partly due to occupants’ patterns of operating their homes with evidence of excessive heating energy use in attempts to alleviate the indoor issues experienced. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

Mosques are external load dominated buildings that are characterized by their intermittent and varying occupancy schedules. Most mosques employ some form of mechanical heating/cooling system in order to maintain suitable thermal comfort conditions for the users during prayer times. Due to the unique spatial characteristics and occupancy patterns of mosques, these systems are often found to be energy-intensive which impacts the overall energy efficiency of the building. The inefficiency in mosque energy usage has been typically attributed to the poor thermal performance of the buildings along with unsuitable operational strategies for the occupancy schedule of mosques. This paper reviews contemporary literature on mosque energy usage with an aim of identifying the factors that influence the energy efficiency of mosque buildings. Findings from the literature have been categorized according to different parameters of the building design as well as design and operational strategies of the heating/cooling systems. Discussion on the common practice and best practice has also been done with respect to thermal comfort standards and requirements. In addition to that, this paper compares and critically evaluates the studies that have aimed at reducing energy consumption and improving energy efficiency in mosques. Findings from multiple research suggest that as much as half of the energy usage can be reduced with the optimization of building design and operational strategies of mosques. The review of contemporary literature provides valuable insights into mosque energy usage patterns and identifies the important aspects to be considered in reducing energy consumption in mosque buildings. Through this literature review, numerous research gaps have been identified that may be pivotal in designing energy efficient mosques. Based on those, future potential research prospects have also been suggested. Image 1 • A comprehensive review on energy performance of mosque buildings is presented. • Factors influencing mosque energy efficiency are identified. • Impact of mosque design and operational parameters on energy usage are addressed. • Critical analysis of energy optimization studies is presented. • Numerous research gaps and scopes are identified for further research. [ABSTRACT FROM AUTHOR]

• Energy performance certificate (EPC) to c contribute in defining a energy poverty index. • Mapping building energy poverty risks to support policy actions for tackling energy poverty. • Merging GIS tool and EPC database to obtain city energy poverty maps at city (Bologna) level. Energy poverty is defined as the condition in which low-income people is no more able to face the costs of energy bills and consequently accept to live in cold and uncomfortable houses. In recent years the scientific literature about this specific issue has registered a significant growth, despite the problem is mainly approached from an econometric point of view to which instead medical, social and energy aspects have to be coupled. Recent EU policies like the Clean Energy Package and the Energy Building Performance Directive Recast III encourage to increase the efforts to tackle and eradicate the energy poverty. The paper approaches the problem focusing on one of its complementary causes: the building energy performance. The adopted methodology includes the calculation of the energy costs for each household, the definition of the energy poverty threshold, the calculation of the related building energy performance limit, otherwise leading to the energy poverty condition. These data, associated with the energy performance certificates, are used to create a GIS based mapping of the buildings potentially affected by energy poverty. Maps can be used to support decision making process in addressing appropriate strategies at urban level to tackle the energy poverty risk. The paper includes a case study in the city of Bologna where the proposed methodology is tested. [ABSTRACT FROM AUTHOR]

Window systems play a key role in establishing both the thermal and luminous environments within buildings, as well as the consequent energy required to maintain these for the comfort of their occupants. Various strategies have been employed to improve the thermal and optical performance of window systems. Some of these approaches result in products with relatively complex structures. Thus, it becomes difficult to characterise their optical and thermal properties for use in building performance prediction. This review discusses the experimental and numerical methods used to predict the thermal and optical behaviour of complex window systems. Following a discussion of thermal characterisation methods available in the literature that include experimental test methods, theoretical calculation methods and Computational Fluid Dynamic methods, sophisticated optical methods, such as use of Bidirectional Scatter Distribution Functions (BSDF) to optically characterise complex window systems, are introduced. The application of BSDF allows advanced daylight assessment metrics along with daylight evaluation tools to be used to realise dynamic annual prediction of the luminous environment. Finally, this paper reviews methods that permit the prediction of the combined thermal, daylight and energy behaviour of buildings that make use of complex window systems. [ABSTRACT FROM AUTHOR]

An investigation of building performance through a comfort and operational quality standpoint is not too often discussed for Net-Zero Energy Buildings (NZEB). It would seem important that predications and claims for reducing energy-use in buildings and the move towards NZEB does not compromise comfort in buildings. A high school in Essendon, Victoria (near Melbourne) Australia undertook a retrofitting process to improve its existing library from a functional as well as an environmental standpoint. In the process, several pre- and post-measurements were made of building performance. An advanced packaged unit mechanical system applies a demand control ventilation system with a specialised control that promises uniform air temperature throughout the volume of the conditioned space. This system utilises staged compressors with variable speed drive fans and results in minimal background noise. For the mechanical system to deliver as promised, the building envelope was air pressure sealed to an international standard and double glazed (from single glass) and zoned according to orientation and spatial function. Efficient LED lighting was also applied to a dimming control responsive to occupancy and daylight. This level of building improvement requires before and after measurement procedures to assure and prove a quality performance for a near NZEB. Various measurements of pressurisation, air exchange rates through tracer gas, air temperature stratification, IAQ, as well as a continuous and localised comfort survey measurement all contribute to a pre and post assessment of building performance. This paper discusses several measurement processes considered useful to characterize the performance of an HVAC system in a retrofitted library space. As a result of the building envelope quality, subsequent monitoring of the mechanical conditioning system showed that it could have been reduced to half its specified size. Furthermore, sub-metering of the space indicates that the attention given to the lighting and conditioning systems has reduced their impact, so that plug loads are beginning to dominate and require attention if net zero energy building is to be targeted. [ABSTRACT FROM AUTHOR]

In this paper, the energy performance of an energy self-sufficient residential building, located in the outskirt of L’Aquila city, in central Italy, is presented. The aim of this study is to demonstrate that even a self- sufficient building, that represents a still uncommon construction in the Mediterranean area, can have optimization margins. The analysis is performed via calibrated model carried out with EnergyPlus™ engine coupled with DesignBuilder. The results of this study show a substantial discrepancy between the high efficiency of the envelope, and the poor performance of the heating system. Therefore, five possible scenarios, that differ for the technological plant, were hypothesized. Based on the total thermal energy consumption of the heating system as it is, equal to 29.9 kWh/m 2  y, the proposed multi-scenario analysis allows to identify a solution that ensures energy savings up to 67.1%. In this study, these results indicate that a proper a priori study of building’s thermal load by considering different plant typologies could help to choose the better solutions for realizing efficient buildings. [ABSTRACT FROM AUTHOR]

Near-zero energy buildings (nZEBs) are considered as an effective solution to mitigating CO 2 emissions and reducing the energy usage in the building sector. A proper sizing of the nZEB systems (e.g. HVAC systems, energy supply systems, energy storage systems, etc.) is essential for achieving the desired annual energy balance, thermal comfort, and grid independence. Two significant factors affecting the sizing of nZEB systems are the uncertainties confronted by the building usage condition and weather condition, and the degradation effects in nZEB system components. The former factor has been studied by many researchers; however, the impact of degradation is still neglected in most studies. Degradation is prevalent in energy components of nZEB and inevitably leads to the deterioration of nZEB life-cycle performance. As a result, neglecting the degradation effects may lead to a system design which can only achieve the desired performance at the beginning several years. This paper, therefore, proposes a life-cycle performance analysis (LCPA) method for investigating the impact of degradation on the longitudinal performance of the nZEBs. The method not only integrates the uncertainties in predicting building thermal load and weather condition, but also considers the degradation in the nZEB systems. Based on the proposed LCPA method, a two-stage method is proposed to improve the sizing of the nZEB systems. The study can improve the designers’ understanding of the components’ degradation impacts and the proposed method is effective in the life-cycle performance analysis and improvements of nZEBs. It is the first time that the impacts of degradation and uncertainties on nZEB LCP are analysed. Case studies show that an nZEB might not fulfil its definition at all after some years due to component degradation, while the proposed two-stage design method can effectively alleviate this problem. [ABSTRACT FROM AUTHOR]

Climatic zoning for building energy efficiency applications is an important element in building energy policy and regulations. There are several methodologies available to conduct climatic zoning, providing significantly different results. Currently, there are no procedures to assess the validity of a proposed climatic zoning, hindering the decision to use one particular climatic zoning methodology instead of another. This paper introduces a quality index and a procedure to support the validation of climatic zoning. The procedure is based on building performance simulation results concerning the building stock that is targeted in the climatic zoning policy or program. Simulation results are used to calculate a new index, the Mean Percentage of Misclassified Areas (MPMA), which assesses the quality of the zoning under analysis. The capabilities of this procedure were demonstrated by the evaluation of four alternatives for the climatic zoning of Nicaragua, obtained using different methodologies and previously reported in the literature. The building stock used in this case study is composed of a few archetypes based on typical naturally ventilated dwellings in this country. Simulations were conducted using the program EnergyPlus for a total of 328 locations in Nicaragua. Degree-hours of discomfort based on the adaptive model of ASHRAE Standard 55 were used as a performance indicator. Results indicate that zoning obtained using cluster analysis and cooling degree-days may misclassify 1 out of 5 areas in Nicaragua (MPMA around 18–20%). This study concludes that the validation procedure and proposed index are useful for highlighting qualities and deficiencies of existing climatic zoning methods, particularly when these methods are used in less conventional applications, such as for policy making targeting naturally ventilated dwellings in tropical climates. The application of this procedure in more than 50 countries which adopt climatic zoning is foreseen as the next step in his area, substantially affecting the prescription of building materials and components worldwide. [ABSTRACT FROM AUTHOR]

The need to improve the energy efficiency of buildings has introduced the concept of nearly zero-energy buildings into European energy policies. Moreover, a percentage of the building stock will have to be renovated annually to attain high energy performance. Conventional passive interventions in buildings are focused on increasing the insulation of the building envelope to increase its energy efficiency during the operating phase. Often, however, intervention practices imply the incorporation of embodied energy into the building materials and increase the associated environmental impacts. This paper presents and evaluates a comparison of two different proposals for a real-world building renovation. The first proposal was a conventional project for energy renovation, while the second was a low-energy building proposal (following the Passivhaus standard). This study analysed the proposals using an integrated life cycle and thermal dynamic simulation assessment to identify the adequacy of each renovation alternative regarding the post-renovation energy performance of the building, including an evaluation of the introduction of a renewable insulation material into the low-energy building proposal, specifically a specific cork solution. The most significant conclusion was the convenience of the renovation, achieving energy savings of 60% and 80% for the conventional and Passivhaus renovations (ENERPHIT), respectively. The former supposed less embodied energy and environmental impacts but also generated less energy savings. The latter increased the embodied impacts in the building, mainly for the large amount of insulation material. The environmental implications of both proposals can be compensated for within a reasonable period of time, over 2 years in the majority of alternatives and impact categories. However, the ENERPHIT project was 30% better than the conventional proposal when the total lifespan of the building was considered. The introduction of cork did not fit the requirements for competing with the common non-renewable insulation materials because it did not imply better environmental performance in buildings, but cork insulation solutions currently present ample room for improvement. [ABSTRACT FROM AUTHOR]

Building performance simulations (BPS) are used to test different designs and systems with the intention of reducing building costs and energy demand while ensuring a comfortable indoor climate. Unfortunately, software for BPS is computationally intensive. This makes it impractical to run thousands of simulations for sensitivity analysis and optimization. Worse yet, millions of simulations may be necessary for a thorough exploration of the high-dimensional design space formed by the many design parameters. This computational issue may be overcome by the creation of fast metamodels. In this paper, we aim to find suitable metamodeling techniques for diverse outputs from BPS. We consider five indicators of building performance and eight test problems for the comparison six popular metamodeling techniques – linear regression with ordinary least squares (OLS), random forest (RF), support vector regression (SVR), multivariate adaptive regression splines, Gaussian process regression (GPR), and neural network (NN). The methods are compared with respect to accuracy, efficiency, ease-of-use, robustness, and interpretability. To conduct a fair and in-depth comparison, a methodological approach is pursued using exhaustive grid searches for model selection assisted by sensitivity analysis. The comparison shows that GPR produces the most accurate metamodels, followed by NN and MARS. GPR is robust and easy to implement but becomes inefficient for large training sets compared to NN and MARS. A coefficient of determination, R 2 , larger than 0.9 have been obtained for the BPS outputs using between 128 and 1024 training points. In contrast, accurate metamodels with R 2 values larger than 0.99 can be achieved for all eight test problems using only 32–256 training points. [ABSTRACT FROM AUTHOR]

Buildings’ performance is a vital aspect of organizational activity which is affected by maintenance procedures and policies. Therefore, facility maintenance plays a crucial role in informing strategic decision making. This research proposes the use of key performance indicators (KPIs) to dynamically model and simulate the performance of the existing facilities. Further, it transforms the descriptive analytics into predictive and prescriptive analytics by assessing the robustness of plans and policies set and by predicting future outcomes, through analyzing of multiple scenarios. Moreover, the research highlights the effect of energy consumption on the operating costs, and how it affects the building performance. For instance, when decreasing energy consumption by 58%, the operating costs can decrease to 55%, taking into account that the operating costs include not only that of energy, but also the cost of materials consumed. Hence, the building performance should increase unless the deferred maintenance has not been completed yet. This paper is part of a research that aims at using System dynamics modeling to quantify the interrelationship and interdependence of KPIs which studies the performance of existing building from three aspects financially, physically and functionally, as it is potentially effective in analyzing how maintenance expenditures can be optimized to maintain the desired level of building performance as demonstrated by several simulation scenarios. All parameters and variables used are quantified in terms of cost and introduced in a dynamic model where the functionality of the KPIs is dependent on the availability of data from the buildings in use. The proposed approach helps facility management professionals not only in tracking the indicators, but also in quantifying and studying the correlation between them based on available information. This leads to enhancing facility management decisions, with measurable facility performance outcomes. [ABSTRACT FROM AUTHOR]

More than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupant behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle. [ABSTRACT FROM AUTHOR]

Buildings accounted for half of global electricity consumption in recent years. Accurate occupancy information could improve the energy efficiency and reduce the energy consumption in built environment. Although prior studies have explored various sensing techniques for occupancy sensing, these solutions still suffer from serious drawbacks, e.g. their estimated occupancy information is coarse, extra infrastructure is required, and the privacy of occupants is exposed. In this paper, we present the design and implementation of a novel and practical occupancy sensing system, WinOSS, which is able to provide fine-grained occupancy information thoroughly by leveraging existing commodity WiFi infrastructure along with the WiFi-enabled mobile devices carried by occupants. We have implemented WinOSS in a 1500 m 2 built environment for four weeks to validate its performance. Extensive experimental results demonstrate that WinOSS outperforms existing occupancy sensing techniques, and provides comprehensive fine-grained occupancy information (including occupancy detection, counting and tracking) in an accurate, reliable, cost-effective and non-intrusive manner. [ABSTRACT FROM AUTHOR]

Energy demand reduction from buildings is widely recognised as a key component of greenhouse gas abatement strategies. As governments shift towards large-scale sectoral interventions, a far more robust research and evidence base is needed to support the development, implementation, and on-going evaluation of energy demand policy. The shift to a low carbon built environment will require both a step change in the energy performance of buildings alongside more efficient provision of energy services, and an aggressive decarbonisation of the energy used. Yet the prerequisite data of building stocks needed to support this essential shift in energy performance of buildings are not necessarily available or are inaccessible or incomplete. As more information on building energy use is collected through high frequency sensors and building form analytics become more sophisticated, the analysis methods applied to the myriad and diverse sub-sectors of the building stock ‘population’ need to be commensurate with the heterogeneity of the building stock. This paper describes and illustrates the basis of the IEA EBC Annex 70: Building Energy Epidemiology, which draws on the health sciences to posit ‘energy epidemiology’ as a whole-system approach for empirical research that provides a methodological framework for building physicists, engineers, social scientists, and economists to engage in cross-disciplinary studies. It makes the case that the development and application of an epidemiological approach to investigating energy demand can advance understanding of the inter-related factors for policy guidance and evaluation and provide insights on the mechanisms that influence energy demand. The aim of the IEA EBC Annex 70 is to work in an international collaboration to identify user needs around energy demand in buildings and to establish best practice methods and harmonized formats for data collection, analysis and modelling. To illustrate this process, we present an example from the UK on the application of energy epidemiological methods to building energy performance in the residential sector. The case study investigates the potential effectiveness of the policy and technical measures proposed by the UK Government. Policy implementation for broad, deep, and urgent reductions in energy demand from the building sector requires a far better understanding of the underlying relationships between people, energy use, buildings and the environment. [ABSTRACT FROM AUTHOR]

• An uncertainty propagation method was developed which captures climate uncertainties. • The method is applicable to cases where few future weather files can be developed. • The method can reduce computation requirements following a probabilistic approach. • The impact of equipment modernization using updated design day files is investigated. In the United States, buildings consume a significant portion of total energy use and contribute to carbon emissions. Therefore, evaluating their performance for sustainable design is critical. While building standards are updated frequently to improve building efficiency, the climate is changing and classical (deterministic) approaches to quantifying building energy use may not be accurate. Significant uncertainties exist in current models of future building performance. Probabilistic approaches to evaluate whole building performance and account for climate uncertainties require large input data of climate projections with detailed spatial and temporal resolution and considerable computational resources. This paper offers a four-step process to propagate climate uncertainties from future climate projections into building energy performance. The climate uncertainty propagation method consists of a combination of regression, distribution fitting and random sampling. It has the potential to capture climate uncertainties in building simulation tools and is applicable to cases where limited future weather files can be produced. In addition, we include the use of updated design day files in future building performance analysis. Design days drive equipment modernization impacting whole building energy use under climate change. The Department of Energy office reference buildings for Philadelphia climate conditions are used as the case study. The aim of this research is to generate a probabilistic model that captures future building energy performance trends where can be applicable to regions that have limited future hourly weather files to be incorporated into building simulation tools. The results of this paper are intended to promote a probabilistic approach to account for climate uncertainties and to provide guidance in analyzing buildings projected energy consumption under climate change. [ABSTRACT FROM AUTHOR]

• An overall panorama of the school buildings in Brazil was presented. • Energy billed data and survey evidence were used to build a statistical stock model. • The Weibull distribution was the most adequate to represent the continuous variables. • kWh/student·year was more suitable than kWh/m2·year to model Brazilian school buildings stock. • Thermal performance is an issue to be greatly improved in schools in Brazil. Understanding the actual conditions in school buildings regarding energy usage and environmental satisfaction gives way to the development of consistent building stock modelling. In the Brazilian context, a deep-rooted inefficient structure of energy management implies a lack of mapping the actual conditions of the public schools. Therefore, this paper aimed to assess the actual energy performance of school buildings in Brazil. This paper innovates by integrating questionnaire responses and billed energy data to allow the construction of an evidence-based stock model through a statistical approach, which was never performed for the Brazilian building stock. Moreover, the association of energy use intensity (EUI) with the core aspects of building characteristics, occupancy, environmental satisfaction and necessary improvements mapped the relevance of such aspects in energy usage patterns. Additionally, a discussion of the EUI indicators was addressed. Results showed an unprecedented broad panorama of the school building stock in Brazil and its main features. In terms of statistical analysis, the Weibull distribution was appropriate for representing the continuous variables of the stock. The EUI as a function of the number of students was more suitable than the floor-plan area to achieve a reliable energy performance analysis. Several tests were performed to assess if the energy management, environmental satisfaction and maintenance practices impacted the EUI or not. Finally, the main conclusion was that the stock model obtained was simple and fit the energy management structure in the Brazilian scenario. [ABSTRACT FROM AUTHOR]

According to the Directive (2010/31/EU), the main goal in the energy performance of buildings, especially for achieving longer-term objectives in the energy saving, is achieving Nearly Zero-Energy Buildings (NZEBs). Thus, this plan can be achieved by refurbishing of existing or by constructing new buildings to satisfy NZEB requirements. Nowadays, refurbishment of existing buildings to satisfy NZEB requirements becomes one of the major aim in construction. In solving this refurbishment problem, Multi-Attribute Decision Making (MADM) methods help here to evaluate existing state of buildings and to compare them with optimal alternative, which equals to NZEB. This comparison allows selecting optimal refurbishment methods to achieve NZEB requirements. However, existing MADM methods do not applied to compare existing alternatives (i.e. buildings) with the optimal alternative (i.e. NZEB), which is based on standards and laws. Usually existing researches present comparison of selected alternatives among each other or with the best alternative from compared, like in the TOPSIS or ARAS methods. Therefore, in this paper, we analyse the concept of a Passive house and NZEB and its applicability in Lithuanian standards and, respectively, define the optimal alternative (i.e. an optimal building). Second, we analyse possibility of extending existing MADM methods to be MADM optimal (MADM-opt). Moreover, we present several modifications how to transform WASPAS, ARAS and TOPSIS by adding optimal alternative. The modified methods were applied for evaluation of 13 apartments. The results show that MADM-opt is useful for the assessment of alternatives and their evaluation according to the optimal alternative. Moreover, it allows determining difference between the assessed alternatives and the optimal alternative. [ABSTRACT FROM AUTHOR]

Integration of CCHP systems into large buildings provides considerable advantages, as far as the economy, energy consumption and environment related issues are concerned. In this paper, the integration of a hybrid CCHP system into a commercial building is studied. The hybrid CCHP system consists of a renewable and a non-renewable CHP component, photovoltaic/thermal (PV/T) panels and internal combustion engine (ICE). Three objective functions are considered based on cost saving, energy saving and emission reduction goals where are determined as annual operating cost ratio (AOCR), primary energy saving ratio (PESR) and carbon emission reduction ratio (CERR). Considering each objective function, the GA optimization is applied in three different approaches as a single objective optimization problem to find the best size of the system components. Then, using Analytic Hierarchy Process (AHP), which is one of the most used Multi-Criteria Decision Making (MCDM) procedures, the most profitable answer is determined from the three answers achieved in the optimization process. The results showed a good performance of the system. In PESR optimization, which is selected as the best system size, values of 32.96%, 17.25% and 14.79% was achieved for AOCR, PESR and CERR, respectively. [ABSTRACT FROM AUTHOR]

The energy efficiency is a top priority of the construction sector. For this reason, many buildings are undergoing a process of thermal rehabilitation in order to reduce their energy consumption. But for high energy performance and well insulated building envelopes, the influence of thermal bridging on the building energy consumption is major. The aim of the paper is to assess the influence of thermal bridges in the process of thermal rehabilitation, using numerical modelling of the thermal field and thermal bridge catalogues. [ABSTRACT FROM AUTHOR]

The study aims to generate Building system performance design optimization for 11 nearly zero energy new building (nZEnB) types in the cold climate conditions for decision making. The coupling evolutionary algorithms with a building dynamic simulation engine has been applied to reach the “true” optimal solutions. Life cycle cost and performance modulation was carried out in the multi-objective computer model. The calculation of cost optimality was performed on predefined 11 reference building types by taking into account prices as they have been paid by the end consumer including taxes. 960 000 simulation variants were calculated for each building type. This paper proposes coupling of multivariate engine with the Pareto analysis for reduction of costs and energy use, while taking in consideration fiscal and resource economy long term trends as well as fossil energy replacement with the renewable ones from investment perspective. [ABSTRACT FROM AUTHOR]

The hollow block ventilated wall is a new kind of ventilated wall, the exhaust air of HVAC system is used as the supply air to flow through the cavity of this wall in this paper. The heat or cool storage in the wall is reduced by using the heat recovery technology; the temperature of internal surface is lowered in summer and increased in winter, and the human thermal comfort is enhanced. A semi-dynamic heat transfer model of the ventilated wall is presented for thermal performance prediction. The dynamic optimized resistance-capacitance (ORC) model and the number of transfer units (NTU) model are joined together to develop the semi-dynamic heat transfer model of this ventilated wall. The experimental study is carried out for validating the semi-dynamic heat transfer model. The maximum relative error is 4.3% for mean internal and external surface temperatures, and it is 2.6% for the outlet air temperature of the cavity. The heat transfer model established for hollow block ventilated wall is fairly reasonable and possesses high accuracy. This model can be used to predict the surface temperature of the hollow block ventilated wall and the heat flux removed by the airflow in the cavity for performance evaluation. [ABSTRACT FROM AUTHOR]

Inter-building effect (IBE), which refers to the shading effect from surrounding buildings, plays an important role in building energy use in high-density cities. However, it is customary to neglect the IBE during energy simulation. Four issues are observed on previous research: 1) it has been controversial whether the IBE significantly affects energy consumption, 2) most studies used low-rise or medium-rise residential buildings as references, 3) most studies were conducted with hypothesised buildings, 4) most studies did not consider window blinds effect on IBE. These issues inhibit the theoretical understanding of high-rise building energy performance in cities and jeopardise the achievement of accurate simulation. This paper thus aims to investigate the IBE on the energy use of high-rise office buildings in Hong Kong as a typical high-density city. The IBE of ten selected real-life office buildings in their real-life communities was analysed. Four scenarios were compared, namely, simulation of building with both IBE and window blinds (base case), simulation of building with IBE but no window blinds, simulation of isolated building with window blinds, and simulation of isolated building without window blinds. The results show that the IBE influences the total energy use by up to 13.1%. Compared with the base case, heating, ventilation, air-conditioning (HVAC) and the lighting consumption deviation with different scenarios fluctuated largely, ranging from −11.8% to 18.8% in HVAC consumption, and from 0.1% to −27.6% in lighting consumption. An offset effect was found between the decreased lighting consumption and the increased HVAC consumption. From the floor-level analysis, with real communities, lighting energy use decreased with height among all the buildings generally. A multi-variant regression model was established to evaluate the relationship between IBE variables and building energy deviation caused by IBE. In addition, the results conclude that shading blinds cannot be overlooked when examining IBE on building energy use. These findings reveal the importance of considering IBE on building energy consumption in high-density contexts on a case-by-case basis. [ABSTRACT FROM AUTHOR]

This study is designed to evaluate possible energy saving opportunities available for Universities Campuses. As an example three vital facilities in Peter the Great Polytechnic University, St. Petersburg, Russia were taken: the sport hall, the main canteen and one of the main dormitories. In this paper, we use the method of post-occupancy evaluation and regard the building environmental performance as the core of the evaluation system. The study implements multiple data collection methods, including a walk-through inspection to identify the extent of deficiencies found in the three facilities and a questionnaire survey to quantify users’ perception and level of satisfaction with the quality of the built environment. The findings obtained were analyzed and reported to indicate the main failures and successes of the facilities’ performance, and describe the degree of users’ satisfaction. It is of practical value to planning, design professionals, building services’ engineers and facilities managers responsible for the design and operation of these facilities. [ABSTRACT FROM AUTHOR]

Sustainable building performance requires the integration of various metrics such as energy consumption, thermal comfort levels, occupants’ wellbeing, and productivity. Despite their interdependence, these metrics have been mostly evaluated independently, overlooking potential tradeoffs that can occur between them (e.g., energy conservation efforts and thermal comfort). In addition, human-related factors such as occupants’ energy consumption behaviors, schedules, and movements between buildings cannot be captured using current commercial building modeling tools. Consequently, simulating the performance of a group of buildings such as in a campus, neighborhood, or city remains very challenging. In this paper, a comprehensive agent-based modeling (ABM) framework is developed to: (1) model an urban area with several buildings along with the movements and actions of people within the environment; (2) calculate key performance metrics such as indoor/outdoor thermal comfort and energy consumption levels; and (3) test and propose strategies to optimize sustainable building operation. This study illustrates the multidisciplinary approach needed to capture various dimensions of sustainable building performance. The framework is then applied to a green campus environment, identifying an energy management strategy that can reduce energy consumption by 19% without compromising occupants’ comfort and wellbeing. [ABSTRACT FROM AUTHOR]

This paper reports on the building performance monitoring and annual energy demand of two homes built side-by-side over an occupancy period of three years. The study compares the results from on-site monitoring against the assumed parameters and calculations from compliance modelling at design stage. It focuses on the differences and impact of occupancy behaviour, weather conditions, quality of construction and operation which contribute to an increase in energy consumption creating a gap in performance between design and actual. The results from the study show disparities in the fabric performance reflecting on the overall consumption of energy. This longitudinal analysis highlights how building performance needs to be evaluated over longer periods in order to fully understand how homes and their occupants operate and consume energy. The impact of the real performance of homes in Scotland over longer periods needs to become standardised, and a mechanism for feedback into regulatory mechanisms and construction practices applied, if carbon emission targets are to be met. [ABSTRACT FROM AUTHOR]

Buildings tend to not operate as intended, and a pronounced gap often exists between measured and predicted environmental and energy performance. Although the causes of this ‘performance gap’ are multi-faceted, issues surrounding data integration are key contributory factors. The distributed nature of the Architecture, Engineering and Construction (AEC) industry presents many challenges to the effective capture, integration and assessment of building performance data. Not all building data can be described semantically, nor is it feasible to create adapters between many different software tools. Similarly, not all building contextual data can easily be captured in a single product-centric model. This paper presents a new solution to the problem based upon a hybrid architecture that links data which is retained in its original format. The architecture links existing and efficient relational databases storing time-series data and semantically-described building contextual data. The main contribution of this work is an original RDF syntax structure and ontology to represent existing database schema information, and a new mechanism that automatically prepares data streams for processing by rule-based performance definitions. Two test cases evaluate the concept by 1) applying the hybrid architecture to building performance data from an actual building, and 2) evaluating the efficiency of the architecture against a purely RDF-based solution that also stores all of the time-series data in RDF for a virtual building. The hybrid architecture also avoids the duplication of time-series data and overcomes some of the differences found in database schemas and database platforms. [ABSTRACT FROM AUTHOR]

In this paper, an energy performance model of a residential building, including the heating and cooling systems, is created with the integration of an automation system. The aim is to identify the effect of the building automation systems on the heating and cooling energy consumption by controlling the working scheme of building systems. A methodology is developed to integrate the diverse building systems such as heat pumps, boiler, and solar collectors with their operational arrangements through an automation system. Three different control scenarios are investigated for the comparison by utilizing different software. The building energy model is prepared with TRNSYS while scenario conditions are written in MATLAB to import into the TRNSYS via a specific component which is performed as a bridge between MATLAB and TRNSYS. Subsequently, a dynamic hourly simulation for altering the setpoint values for indoor thermal conditions is integrated by application of Fuzzy logic toolbox from MATLAB. The first scenario is the Base Case (BC) which has a simple working principle. The operational structure is based on if/then relation. As a result, the energy consumption for heating and cooling are 88.16 kWh/m2 and 21.57 kWh/m2 respectively. The second scenario is called the application of Information and Communication Technology (ICT), which has more complex conditions. The last scenario, a dynamic hourly simulation which was performed by changing the setpoint temperature values within a specified range instead of using fixed seasonal setpoints. The aim was to improve energy efficiency while providing comfort conditions in the building with the dynamic setpoints and get more accurate results. The analysis shows that the consumptions are decreased 10.37% to 82.71 kWh/m2 while 4.46% is only from the dynamic setpoint changes for heating, and 14.88% to 20.26 kWh/m2 while 9.39% % is only from the dynamic setpoint changes for cooling. • The paper identifies and improve the effect of the building automation systems. • Diverse building systems with their operational arrangements were controlled and simulated. • Fixed seasonal set-point temperature values were changed within a certain range. • It provides a sequence of procedures for stipulating simulation of dynamic set-points. [ABSTRACT FROM AUTHOR]