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• Affordable and adaptable VIP based internal and external retrofitting solutions. • Implementation of VIP based integral retrofitting solutions in two tertiary buildings. • Space heating energy saving analysis before and after VIP based retrofit. • Envelope heat loss coefficient reduction analysis before and after retrofitting. Vacuum insulation panels (VIPs) represent a huge opportunity to achieve nearly zero energy buildings, especially in the retrofitting of existing buildings. VIPs can be applied in conjunction with façade cladding elements, considerably reducing the heat losses or gains from the building by means of very slim wall construction systems or retrofit solutions. However, the fact that VIPs cannot be cut on-site, can be easily damaged during their handling, installation and use and have an envelope that can cause thermal bridging in the junction between panels or when combined with other materials, has hindered their penetration in the construction market. This paper presents two innovative façade retrofitting solutions, a rear ventilated façade and a stud mounted internal solution, that have tackled all these issues and provide thermal transmittances of 0.28 and 0.14 W/m2 K in relatively slim insulation systems of 10 cm widths. Solutions have been installed on the entire façade of two existing buildings located in Bilbao (Spain) and Malmö (Sweden) subjected to an integral rehabilitation process, covering 2100 m2. Buildings energy use have been monitored before and after the renovation, showing energy cuts of around 35% from pre-intervention status. This paper presents the lessons learnt during the design and installation process of the vacuum insulation integrating solutions in the façade of two existing buildings and the thermal performance results achieved after their retrofitting. [ABSTRACT FROM AUTHOR]

This paper analyzes electricity use in buildings and explores key research questions related to assessing, rating, labeling, and benchmarking building energy use. The analysis reviews annual Energy Use Intensity (EUI) data from 3060 fully electric-powered buildings across six categories: residential, educational, offices, hospitals, hotels and restaurants, and wholesale and retail trade services. One-fifth of the analyzed buildings is derived from an energy dataset that offers a higher time-scale resolution, providing more detailed insights into energy usage patterns. The analysis employs various factors, including the seasonal variation factor (SVF), daily variation factor (DVF), monthly factor (M f), and hourly factor (H f), to gain a detailed understanding of actual energy usage at different time scales. The findings indicate that electricity use is less sensitive to seasonal variations than other energy carriers that provide heating and cooling. The paper provides hourly and monthly peak load percentages (PPL), which can indirectly assist in assessing hourly schedules for building energy performance simulations. The monthly energy use pattern varies between building categories, with high-energy months alternating between winter and summer. For non-residential buildings, high-energy hours correspond directly to the daytime, while for residential buildings, there is a shift towards the evening, and the daily amplitude is lower. Furthermore, the analysis of daily and monthly variability of heating energy carriers, even if non-electric, sheds light on the implications of electrification for these loads. • The study evaluates annual electricity data from 3060 fully electric-powered buildings. • Median EUIs are meaningful energy performance benchmarks by building category. • Residential and educational buildings exhibit EUIs of 90-120 kW h/m2. • Offices and hospitals demonstrate EUIs of 160-170 kW h/m2. • Monthly and hourly factors establish energy benchmarks for finer time resolution. [ABSTRACT FROM AUTHOR]

Building performance simulation is a powerful tool applied both in research and for building design including heating, ventilation, and air-conditioning systems. Several research studies have used this tool to investigate the potential of energy savings measures for swimming facilities. However, in these facilities the technical complexity is a considerable challenge in modelling since many complex phenomena occur in the various sub-systems. The building industry traditionally plans and design these complex buildings by heuristic rules of thumb, and these empirical design rules may lead to significant differences between real and predicted performance. Therefore, it's important to develop simplified models with acceptable accuracy for simulation-based design of swimming facilities. The scientific literature introduces different simplifications for the air handling unit modelling, but the impact of these simplifications is hardly analyzed. Therefore, the paper investigates the model complexity of the air handling unit in a Norwegian swimming facility by comparing a novel simplified model and a detailed model which serves as a digital replica of a real unit. The simplified decoupled model is found to have an acceptable accuracy for early-stage design, bearing in mind the overall uncertainty at this design stage. However, when carrying out a detailed investigation of a swimming facility, the detailed model should be applied, since this complex heuristic rule-based model, is taking into account phenomena like thermal-coupling effects between the swimming pool and the air handling unit and the control errors of the real internal controller. In conclusion, the paper derives useful guidelines for building performance simulation design of air handling unit, which is the main energy-intensive component in indoor swimming pool facilities. [ABSTRACT FROM AUTHOR]

• Overheating regulations and calculation methods in 26 European countries were compared. • Most of the existing calculation methods are outdated and do not fit climate-proof buildings. • France requires a mixed-mode operation of naturally ventilated households. • The UK developed a heatwave-based calculation approach. • Comfort-based, multi-zonal, and time-integrated calculation approaches are needed. With the ongoing significance of overheating calculations in the residential building sector, building codes such as the European Energy Performance of Building Directive (EPBD) are essential for harmonizing the indicators and performance thresholds. This paper investigates Europe's overheating calculation methods, indicators, and thresholds and evaluates their ability to address climate change and heat events. e study aims to identify the suitability of existing overheating calculation methods and propose recommendations for the EPBD. The study results provide a cross-sectional overview of twenty-six European countries. The most influential overheating calculation criteria are listed the best approaches are ranked. The paper provides a thorough comparative assessment and recommendations to align current calculations with climate-sensitive metrics. The results suggest a framework and key performance indicators that are comfort-based, multi-zonal, and time-integrated to calculate overheating and modify the EU's next building energy efficiency regulations. The results can help policymakers and building professionals to develop the next overheating calculation framework and approach for the future development of climate-proof and resilient residential buildings. [ABSTRACT FROM AUTHOR]

[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]

[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 data-informed building energy management (DiBEM) framework was proposed. • It was demonstrated in an office/laboratory building with two-years' experimental data. • The impacts of energy efficient interventions were quantified through data-driven models. • The energy use intensity was reduced from 54.1 kWh/m2 to 42.8 kWh/m2. To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years' building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants' behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants' requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy-efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations. [ABSTRACT FROM AUTHOR]

[Display omitted] • nZEBs in Eastern Europe lack carbon neutrality metrics and targets. • Performance requirements for nZEBs in Eastern Europe are presented. • Eastern Europe needs strategy and local governance for nZEB market penetration. • EPCs require major reform, and effective monitoring of nZEB performance is needed. • The cost-optimality calculation method must be adopted to ensure cost-effective market uptake. The European Unions' ambition for the construction sector is to be carbon neutral by 2030 for new construction. Since 2021, all new buildings in the EU should have been constructed as nearly zero-energy buildings (nZEB). However, Eastern European countries struggle to implement the 2018 Energy Performance of Building Directive recast requirements. Next to the economic challenges, equally essential factors hinder renovating the existing residential building stock and adding newly constructed high-performance buildings sourced primarily from renewable energy sources. Therefore, this study provides a cross-study to identify the barriers to nZEB implementation in ten Eastern European countries, including Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia. The study was conducted between 2019 and 2021 and provides an overview of prospects for nZEB in Eastern Europe. The study examines the challenges of nZEB plans faced in those countries and provides constructive recommendations. The regulations and definitions regarding nZEB energy performance, cooling and heating energy demand, thermal comfort, onsite renewables, and construction quality were analyzed. Results show that most Eastern European countries are unprepared to comply with the EPBD guidelines and cost-optimality approach. The paper ranks each country and recommends specific measures to refine the nZEB definitions. The paper provides a thorough comparative assessment and benchmarking of select EU geography that can help shift the identified gaps into opportunities for the future development of climate-neutral high-performance buildings. [ABSTRACT FROM AUTHOR]

The energy consumption necessary to maintain thermal comfort in buildings remains a significant and partially unjustified part, accounting for about 30–40% of the total energy consumption. Despite the increasingly pressing regulations in the energy sector (EPBD recast), the quality of the internal environment remains one of the critical parameters on which to pay more attention in view of a consistent evaluation of the Nzeb building. The windowed components are often designed with thermal insulation performances higher than the minimum requirements but still they lack in design of the incident solar radiation control, which is a particularly delicate topic for indoor comfort and more generally for a higher energy efficiency in buildings. If we think of the numerous variables of influence (typology, orientation, geographical location, etc.), the possibilities to associate energy saving, lighting control and users' comfort are inevitably entrusted to dynamic systems integrated into the glass. In this paper authors present the results related to the experimental characterization of two identical test rooms with different typologies of windows. One room is furnished with a traditional high thermal performance glass and the other, with an electrochromic glass suitably controlled by remote. The operative temperature measured in both test rooms have been utilized to verify the simulation model developed to assess the energy saving achievable in a Mediterranean climate, taking properly into account the hourly variations of the climate and the actions on the shading regulation systems. [ABSTRACT FROM AUTHOR]

Abstract Convenience stores are known to have higher Energy Use Intensities (EUIs) than most other commercial building types. However, convenience store energy performance analyses have not been systematically investigated. Moreover, current national convenience store benchmarks are inconsistent from one data base to another due to lack of relevant energy data. This paper assesses the energy performance of more than five hundred convenience stores in the same fleet in Eastern United States. The investigation aims to establish benchmarks for convenience stores with and without gasoline pumping stations. The main factors influencing the actual convenience store building energy use were determined. Multiple regression models were developed to find relationships between annual energy consumption per unit area and gross floor area, customer transaction count, building operating year, etc. The statistically characterized results are meant to serve as a reference data set in comparing the whole-building energy performance index for buildings of similar functionality. The statistical correlation analyses can strengthen the robust prediction of energy performance in convenience stores, enabling proper assessment of energy conservation measurements (ECMs) for convenience store operations, as well as guiding additional such energy performance studies. [ABSTRACT FROM AUTHOR]

• Bottom-up archetype model for space heating demand of Swiss office building stock. • Archetypes characteristics derived from energy certificates database. • Estimation of current thermal performance and energy retrofit's potential. • Calculation of national heating demand and distribution among archetypes. • Influence of design characteristics and buildings properties on heating demand. Energy efficiency in buildings is a key objective of the European energy strategy. To address the limited knowledge of the thermal performance of the non-domestic building stocks, this paper presents a bottom-up, physics-based, archetypal model derived from a large building stock database and physical characteristics from 2170 building energy performance certificates in Switzerland. The model was validated by measured energy consumption data and national statistics. Results highlight the potential for energy retrofit: most of the stock is still heated with fossil fuels (67% of the total heated area) and 40% of the total envelope surface has a poor thermal performance associated with the period before 1980. We estimate national final energy demand for heating of the office building stock (11.3 PJ/year) and present detailed breakdown of energy by thermal loss element. Significant differences were found between archetypes related to urban settings (specific demand 50% higher in rural than in urban contexts) and age of the buildings (56% of the total demand is due to buildings constructed before 1970). The physical model highlights the impact of architectural design characteristics such as building compactness and glazed surface ratios on thermal performance. The model represents an important step for studies on optimal thermal retrofit pathways. [ABSTRACT FROM AUTHOR]

• Investigated climate change impacts on office building performance in Brussels. • Applied a long-term climate change sensitive system sizing and design approach. • Time-integrated discomfort analysis showed a major surge in overheating by 2100. • Long-term climate change resistivity is ensured in reference building until 2100. • Primary energy use and GHG emission findings are useful in mixed humid climates. As climate change continues, it is expected that the risk of overheating will rise in both new and existing buildings in mixed humid climate zones in Europe. This study introduced a novel climate change sensitive sizing and design approach for cooling and heating systems in nearly zero-energy office buildings in Brussels, Belgium, for different weather scenarios. This approach considered the long-term effects of climate change on building performance. The climate change effects were assessed using current and future climate data from the regional atmospheric model, MAR. To demonstrate the approach, a case study of a nearly zero-energy office building in Brussels was conducted. The reference building model was first calibrated using monthly energy use data from the year 2019 using ASHRAE Guideline 14. Then, the building was evaluated with different HVAC strategies and their performances were quantified. The results indicated an increase in overheating as high as 1.2 °C and cooling energy use as high as 13.5 kWh/m2 and a decrease in overcooling as low as 0.3 °C and heating energy use as low as 10.9 kWh/m2 in the reference building by the end of the century. In addition, due to climate change sensitive sizing and design approach coupled with optimal sizing, the reference building was climate change resistant towards the worst-case scenario by end of the century with up to 3.7 for climate change overheating resistivity and 20.2 for climate change overcooling resistivity. Finally, the paper provided recommendations for future practice and research based on the study findings. [ABSTRACT FROM AUTHOR]

• Building performance simulation is powerful but computationally expensive. • Machine Learning (ML) surrogate models can reduce simulation times significantly. • Building energy and peak loads are predicted under different operational settings. • Extreme gradient boosting outperformed other methods. • Linear regression showed the lowest training time with competitive predictive power. Building Performance Simulation (BPS) is a powerful and widely used technique to evaluate building design and operation strategies prior to construction or retrofitting. However, BPS models often have high computational costs, which is particularly limiting for applications that require a significantly large number of simulations, such as building design optimization or uncertainty analyses. To overcome this gap, researchers have turned to surrogate modeling, where a mathematical model, such as a machine learning algorithm, is trained to mimic the performance of a BPS, allowing to test numerous building design/operation configurations at low computational costs. Past studies have applied surrogate BPS modeling to predict the impact of building design parameters on energy performance. However, few have considered building operational parameters, such as occupancy, equipment and lighting usage, and thermostat setpoints, which significantly impact energy consumption and peak loads, especially in harsh climate conditions. This paper presents a unique evaluation and comparison of machine learning algorithms as surrogates to BPS predictions of building performance (energy consumption and peak loads) under different operational settings. Results indicate that Extreme Gradient Boosting outperformed all other methods in predictive accuracy, with R2 values reaching as high as 0.99 for some models. In contrast, linear regression models were the fastest to train and easiest to interpret while still achieving competitive prediction accuracies (R2 values > 0.9). This work provides direct evidence of the machine learning surrogate models' ability to accurately predict building performance under different operational settings. It also offers unique insights into the strengths and weaknesses of white-box and black-box predictive modeling approaches and the effect of dataset size on the results. [ABSTRACT FROM AUTHOR]

Lighting energy savings, as well as visual and non-visual user benefits have been widely attributed to daylighting. This paper explores daylight design strategy, visual appraisal, Daylight Factor (DF), lighting energy usage and discomfort glare using two green building offices in Malaysia, which have incorporated daylighting into both façade and interior design. Visual appraisal surveys were collected from 39 and 145 subjects in the open plan working space of the Energy Commission Building (ECB) and Public Works Department Block G (PWD), respectively. The survey focused on task brightness, colour appearance, uniformity and lighting preference. Discomfort glare assessed via occupant point-of-view luminance maps was juxtaposed here from a glare study involving the same buildings. Illuminance loggers were used to monitor artificial lighting usage as well as the DF on a selected floor of each building. There were no significant differences in occupant responses to the visual appraisal survey for both office spaces. Using MS1525:2014 and Green Building Index (GBI NRNC) tool as baselines, the DF performance of both offices differs significantly: PWD had a 45.5% daylit area, with ECB a 14.8% daylit area for DF >1%. However, lighting energy usage results show substantial savings of 53% and 41% occurred from daylighting. These findings of visual appraisal, DF, lighting energy savings and discomfort glare show a discrepancy in using only the DF to justify the daylight performance of an office space in a tropical climate such as Malaysia. The findings suggest that equivalent consideration should be given to interior design to facilitate daylighting, which is often beyond the control of designer, but in the hands of office end users. [ABSTRACT FROM AUTHOR]

• EU policy efforts on energy efficiency in buildings stared in the 1970s in response to the oil crisis. • The first comprehensive EU policy was the SAVE directive in 1992, introducing policy actions still relevant today. • A major step forward was the Energy Performance of Buildings Directive in 2002 and its subsequent amendments. • Mandatory energy performance standards are progressively converging towards near zero energy buildings. • Additional policies and financing are needed for the full decarbonisation of the building stock. The reduction of energy demand in buildings through the adoption of energy efficiency policy is a key pillar of the European Union (EU) climate and energy strategy. Energy efficiency first emerged in the EU energy policy agenda in the 1970s and was progressively transformed with shifting global and EU energy and climate policies and priorities. The paper offers a review of EU energy policies spanning over the last half century with a focus on policy instruments to encourage measures on energy efficiency in new and existing buildings. Starting from early policies set by the EU in response to the Oil Embargo in the 1973, the paper discusses the impact of EU policies in stimulating energy efficiency improvements in the building sector ranging from the SAVE Directive to the recently 2018 updated Energy Performance of Buildings Directive and Energy Efficiency Directive. The review explores the progress made over the last 50 years in addressing energy efficiency in buildings and highlights successes as well as remaining challenges. It discusses the impact of political priorities in reshaping how energy efficiency is addressed by EU policymakers, leading to a holistic approach to buildings, and provides insights and suggestions on how to further exploit the EU potential to save energy from buildings. [ABSTRACT FROM AUTHOR]

• A review of simulation-aided occupant-centric building design studies is presented. • Covered themes include occupant-centric metrics, models and design practices. • Most studies focus on energy-related metrics with a lack of implementation. • Metrics related to occupant comfort, wellbeing and space planning are understudied. • Improved data collection, modeling and communication methods are recommended. Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices, including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Future research directions are discussed, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, improved metrics of measuring building performance, and improved industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process. [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]

• This method integrates uncertainty analysis and cluster analysis methods. • Proposed novel zoning indicators are statistically significant. • Moves from traditional weather-based approaches to a performance-based approach. • Robust tool to tackle complex relation between climate and building energy efficiency. • Provide evidence for the rationality of decision making in energy policy. Climatic zoning is an important tool in building energy policy and regulations, smaller and more homogeneous climate zones could help enhance the rationality of energy policymaking. In most countries, climate zoning is based on simplified climate models, and it is difficult to accurately represent the energy demand characteristics of buildings. Therefore, this paper proposes a novel climate subdivision method that tackles the complex relations between climate and building energy-efficient, which move from traditional weather-based approaches to a performance-based approach. Integrate the uncertainty analysis method and the building dynamic performance simulation to extract a novel statistically significant index as the basis for classification. The K -means clustering method and discriminant analysis method are used to obtain and verify the clustering results, respectively. Furthermore, the comparison with clustering results based on degree days confirms the superiority of the new method proposed in this paper in identifying building energy demand. The method is applied to the cold climate zone of China as a showcase. The results show significant differences in building energy demand and the order of key features affecting building performance between each region in the climatic zones. The zone can be further sub-divided into four groups considering the climate factor and building feature uncertainty, and clustering results have been verified by several evaluation indicators. [ABSTRACT FROM AUTHOR]

• New building development projects underperform often. • Underperformance leads to increased energy consumption and CO 2 emissions in building operation. • The paper links building project development and building operational performance. • Two simulation methods are combined in a multi methodology framework in the paper. • The paper explores the implications of project partner collaboration for operational building performance in the case of a recent building project. The "performance gap" in the United Kingdom construction industry is a persistent problem as new building development projects underperform more often than not. The "performance gap" is partially attributed to the number of stages involved in building project development and the coordination difficulties of partners with different incentives. The project outcome is important for energy consumption, carbon emissions and occupant well-being. Thus, it is important to study the project management process in terms of the standard time, cost and quality parameters, but also in terms of project partner incentives and coordination, and the subsequent energy performance and resultant indoor environmental conditions. A system dynamics model of project management processes is developed to explore the implications of partner coordination for building quality. The system dynamics model is coupled to a building performance simulation model to explore building energy consumption and Indoor Environmental Quality, and apply this on a recent building project case study. Results show that greater project partner alignment can reduce annual energy consumption up to 12% and CO 2 emissions up to 37%, with greater emphasis in the design stage of the project subject to resource availability. The trade-offs involved on value appropriation are considered and discussion of results points to possible ways for improvement. [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]

[Display omitted] • The strengths and weaknesses of existing data collection methods are analyzed. • The main correlated factors for different building types are summarized. • Existing methods to characterize behavioral diversity are classified and compared. • The adaptability of various prediction models is discussed. • The mutual validity of model evaluation indicators is examined. Opening windows is by far the simplest means of ventilation. It is also crucial for energy efficiency and for creating a comfortable indoor environment. During the last decade, researchers have made great strides in investigating occupants' window operation patterns. However, the different research methods used to capture, recognize, and model behavior across the literature make it difficult to compare the results between studies. This has greatly impeded the application of window opening behavior models to building energy performance simulation software. Therefore, this paper aims to provide a comprehensive review of occupants' window opening behavior in relation to four aspects. The first aspect is data collection methods, whereby different data collection methods are analyzed, and the limitations of the existing monitoring devices are pointed out. The second aspect is the factor analysis, whereby the main correlated factors for different building types are summarized. In particular, it is assessed whether indoor temperature and indoor CO 2 concentration are drivers of the window opening behavior. The third aspect is the characterization of behavioral diversity, whereby the existing methods to characterize behavioral diversity are classified and compared. The final aspect is prediction models, whereby the adaptability of various prediction models and the mutual validity of model evaluation indicators are discussed. In the future, scholars will need to make efforts to expand the test sample size, quantify window opening angles, consider the diversity of behavior and unify model evaluation metrics. This would help the application of predictive models in supporting building design and the evaluation of the effectiveness of energy efficiency technologies. [ABSTRACT FROM AUTHOR]

• POE was carried out to compare the design target and operational effect after use. • A Large Irregular Green Commercial Building was chosen for in-depth analysis. • We recommend the building energy saving and carbon emission reduction strategy. Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating performance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and analyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building performance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO 2 /m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM 2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfaction was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions. [ABSTRACT FROM AUTHOR]

Typically the effect of thermal bridges on the energy performance of buildings is taken into account by implementing an average U-value in 1D whole building energy simulation program, which is referred to as the equivalent U-value method. This treatment accounts for the effect of thermal bridges on the overall thermal transmittance of envelope details, while their thermal inertia effect is ignored. In this paper, the dynamic effect of balcony slab as thermal bridges on the energy performance of a high-rise residential building is investigated by comparing the annual space conditioning loads obtained by the equivalent U-value method to that obtained from 3D dynamic modeling of the balcony junctions implemented in WUFI Plus program. The simulation results show that the equivalent U-value method underestimates the annual heating loads by 2.8–4.4% for the building as-designed. With the increase of balcony portion and better insulated walls, the difference in annual space heating loads modeled using these two methods is increased to 8.6–15.2%. With the implementation of balcony thermal break the annual heating loads are reduced by 8.8–25.7% and the difference between these two modeling methods is also reduced to 1.9–11.5% for the cases investigated. [ABSTRACT FROM AUTHOR]

• A top-down analysis of the non-residential building stock in Brazil is proposed. • The share of each building typology in the country energy consumption was assessed. • A statistical analysis showed variation of EUI according to type of building and region. • Reference values for the commercial building stock in Brazil is provided. This paper provides a pioneering, top-down analysis of the non-residential building stock in Brazil. A country-wide dataset was used, comprising 10,000 buildings divided into 12 typologies. Key indicators include floor area, energy use intensity, and operation patterns. In addition, predominant wall constructions, lighting systems, equipment, water heating, and HVAC systems for each typology were identified, including average density power for the systems. Data from the Brazilian Energy Balance report was correlated with the sample of buildings to determine the proportion of each building typology in the total energy consumption of the country, considering different energy sources. This study provides an innovative and up-to-date status of the characteristics of non-residential buildings in Brazil. A complementary statistical analysis addressed the correlation between energy use intensity and the building characteristics including an ANOVA test and a regression analysis in order to investigate the influence of the energy usage indicators (Lighting Power Density, Equipment Power Density, Cooling capacity) on the energy use intensity of each building typology in Brazil. This study is intended to serve as a reference for building characteristics and building energy performance in Brazil, and hence support diverse research regarding country-level building stock modeling, energy benchmarking, climate change impacts, and energy-efficiency related studies. [ABSTRACT FROM AUTHOR]

• Meter-level anomaly detection and diagnosis is performed through CMP algorithm. • CMP parameters are automatically set using supervised and unsupervised methods. • A severity score, based on 4 statistical methods, is used to recognize anomalies. • 55 anomalous energy consumption patterns are discovered in a one-year timeseries. • Anomalies at meter-level are diagnosed by exploiting information at sub-load level. Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-related data providing accessible knowledge on the actual performance of buildings during their operation. A proper analysis of such data supports energy and facility managers in spotting valuable energy saving opportunities. In this context, anomaly detection and diagnosis (ADD) tools allow a prompt and automatic recognition of abnormal and non-optimal energy performance patterns enabling a better decision-making to reduce energy wastes and system inefficiencies. To this aim, this paper introduces a novel meter-level ADD process capable to identify energy consumption anomalies at meter-level and perform diagnosis by exploiting information at sub-load level. The process leverages supervised and unsupervised analytics techniques coupled with the distance-based contextual matrix profile (CMP) algorithm to discover infrequent subsequences in energy consumption timeseries considering specific boundary conditions. The proposed process has self-tuning capabilities and can rank anomalies at both meter and sub-load level by means of robust severity score. The methodology is tested on one-year energy consumption timeseries of a medium/low voltage transformation cabin of the university campus of Politecnico di Torino leading to the detection of 55 anomalous subsequences that are diagnosed by analysing a group of 8 different sub-loads. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

A great number of low carbon technologies have been developed in the last decades. The use of such technologies promises good indoor environment for the building's occupants, lower energy consumption and carbon emissions. However, there are uncertainties related to payback periods and usability constraints of low carbon technologies. In addition, past and current research has shown differences between the expected and the actual performance of buildings, both in terms of energy consumption and indoor comfort. For a better understanding of differences between expected and actual performance of buildings, it is necessary to investigate the energy consumption, the behaviour of the occupants and their interaction with new technologies, and the resulting indoor comfort. Monitoring building performance can help to reduce the uncertainties related to the building performance of buildings. This paper presents an overview and classification of data collection methods to monitor the energy consumption, thermal comfort of occupants and the operation of buildings in use. Data collection methods and techniques are categorised and linked to specific methods to evaluate building performance and recommendations are made to set up building monitoring research. By identifying the most effective methods to monitor buildings, this research aims at embedding performance monitoring in standard practice. [ABSTRACT FROM AUTHOR]

Moisture-related issues in buildings can accelerate the decay of building materials and increase occurrence of mould, resulting in significant economic and health implications. Transient hygrothermal risks assessment proved to be the most reliable approach, yet its application at early design stages is impeded by the high number of assumptions, input and skills required to be robustly applied. Climate-based indicators can assist in determining effective prevention design strategies based on the magnitude of the potential hygrothermal risks. However, in Australia, the National Construction Code relies on a climate zoning originally developed for thermal and energy efficiency provisions, clustering locations that present different moisture profiles. This paper investigates the suitability of the existing climate zones for hygrothermal risk indications by calculating the moisture index for several representative cites across Australia. Results reveal the current climate mapping to be unsuitable and too coarse to reliably provide robust indications for moisture management. Further, a new characterization of Australian climates for hygrothermal assessment purposes is proposed as a first step towards a better representation of moisture risks within the building code. [ABSTRACT FROM AUTHOR]

[Display omitted] • A research topic relatively new but with an increase interest in the last five years. • The largest scientific contribution from China, Australia and Italy. • The main journals in the field Building and Environment and Energy and Buildings. • Three main direction of research: thermal energy performance of buildings, ecological benefits and reducing the urban heat. • The main limitation of using green walls is the high cost of installing and maintenance. The continuous state of development of the human society imply a big effort and demand to generate and use more and more energy to power our lifestyle. The impact of human activity has negative effects on the environment and thus solutions to maintain or increase our development rate but in the same time to decrease the pressure on the environment must be developed. In this context, the purpose of this paper is to investigate if the use of green walls systems can be a solution to decrease the energy demands in buildings. The investigation include a bibliometric literature research, a scientometric analysis and an in-depth discussion. The data used for the research were obtained by interrogating the ISI Web of Science and Scopus databases, were selected using the guidelines of the PRISMA method and processed with the help of VOSviewer and Bibliometrix software. The research results indicate an increase interest in this topic in the last five years and that the use of vegetation in the constructed grey areas can bring a lot of benefits especially from environmental point of view. Green walls can be a solution to decrease the temperature on the wall surface in hot summer days and to prevent heat loses in the winter time, and thus have beneficial effects on the amount of energy required to maintain a proper interior temperature. The main limitation of using green walls is given by higher costs of installing and maintain the vegetation on walls, thus, finding new more economical, systems of green walls must be developed in the future. Considering all of this it can be state that green wall systems have the potential to become good solutions not just to improve the energy efficiency of buildings but also to create more sustainable urban areas. [ABSTRACT FROM AUTHOR]

Building energy use benchmarking is the process of measuring the energy performance of buildings relative to their peer group for creating awareness and identifying energy-saving opportunities. In this paper, we present the design and implementation of BEEM , a data-driven energy use benchmarking system for buildings in Singapore. The peer groups for comparison are established using a public energy disclosure data set. We use an ensemble tree algorithm for accurately modeling building energy use and for identifying the most influential factors. Our models reduce the prediction error from 24.39% to 6.04%, on average, when compared to the baseline linear regression models, which were used in the previous energy efficiency labeling program in Singapore, and outperforms ten other recent models. Using the prototype implementation of BEEM, we benchmarked three building types, office (290), hotel (203), and retail (125), and compared their rating. The code repository and the accompanying data set are released as an open-source project for community use. [ABSTRACT FROM AUTHOR]

• The research method has three main steps. 1. Experimental study, 2.Survey study and 3. Modelling and simulation for long-term energy and daylight analysis. • In the modelling process 5 blind strategies are investigated as BSDF materials by Five-Phase matrix method considering energy and daylight analyses. • The subdivided window blind strategies showed energy efficiency up to 40% considering occupants satisfaction. • The evaluation of daylight and glare metrics showed that the DGP(low light)<0.45, DGPs 0.2-0.28, UDI300-3000 and DAv300,50% indices support the occupants' visual comfort in the spaces with shaded window. • Occupants' working-place, their field of view and daytime are the important parameters for evaluating daylight performance in the room and selecting the most appropriate blind strategy. Thermal and lighting performance in buildings depend on their facade characteristics, and are important factors to achieve environmental sustainability. Application of an integrated model that balances these two performance indices would help optimise window and shading configurations by considering the comfort of occupants. As occupants infrequently operate shadings in shared spaces, the energy efficiency and visual comfort could be improved by planning appropriate shading strategies in initial design process. Thus, this paper presents a multi-stage study on the control strategies of venetian blinds by considering visual comfort and daylight metrics, and through energy analyses. First, the case study was performed in a south-oriented daylit office room. 30 people were interviewed via a survey about working on a laptop considering three different times of a day, five blind types, and three positions. The impact of daylight on their visual comfort was surveyed at different illuminance levels which were measured by daylight loggers. The useful daylight illuminance (UDI), Daylight Availability (DAv), spatial daylight autonomy (sDA), annual sunlight exposure (ASE), and simplified daylight glare probability (DGPs) were used to evaluate the results. Finally, the window and blinds were modelled for different window to wall ratios (WWRs) for long-term energy and daylight analysis, by considering bi-directional scattering distribution function (BSDF) materials and using the five-phase matrix method. Therefore, the positions of the occupants and their activities were determined to be important parameters in deciding the most suitable blind strategy. According to occupants' working place and daytime, subdivided shadings are recommended, particularly in 90% WWR considering both energy efficiency (40% savings) and satisfaction of occupants. [ABSTRACT FROM AUTHOR]

• In-situ monitoring allows for a more reliable estimation of envelope performance of residential buildings. • Policy differences are one of the main causes for the variation in building envelope performance across regions. • Potential global warming impacts ought to be considered when pondering on the future direction of envelope development of residential buildings. • Interactive policy framework underscores the necessity of devising more comprehensive public policies pertaining to energy saving. Climatic conditions can exert significant impacts on energy demand of residential buildings. Therefore, case-based evidence is necessary for aiding local policy making on building energy performance. By designing and carrying out in-situ monitoring campaigns respectively in Cambridge, Hong Kong and Shanghai, we present in this paper a cross-regional analysis of actual building envelope performance in these three climatic regions that operate under different energy efficiency policies. Compared with conventional tabulated data, information gleaned from the method of in-situ monitoring shows lower uncertainty and higher precision, especially when the wall has good thermal properties. By comparing practices in different regions and with accurate post-occupancy thermal data, the analytical results further show that the effectiveness of building-related policies on energy performance varies across regions. This is one of the pioneering studies that make interactive policy recommendations based on the evidence obtained from cross-regional, in-situ monitoring campaigns. [ABSTRACT FROM AUTHOR]

• The parameters related to NV performance are identified and optimized. • Activation threshold temperature is not the key parameter for NV performance. • A general NV saves up to 82.5% energy but may cause overcooling penalty. • NV optimization improves thermal comfort and further saves up to 38.6% energy. • Optimization of natural NV improves the thermal comfort although it consumes more energy. The rising cooling demand and overheating in the building sector, especially in office buildings, have intensified research interest in recent years. Night ventilation (NV) as a passive energy technology has shown a great potential cooling energy and ameliorate indoor thermal environment. In this paper, a holistic approach involving global sensitivity analysis and evolutionary optimization is developed to exclude insignificant parameters and explore optimal NV performance in terms of energy use and thermal comfort. The proposed approach is based on the simulation research of a three-story office building equipped with daytime air conditioning and NV system in a cold climate region. The NV system is equipped with three levels of specific fan power (SFP), representing cases with natural NV and medium and high SFP mechanical NV, respectively. The results show that the activation threshold temperature is not the key parameter for NV performance. Comparing with the case without NV, the three SFP NV systems under a general scheme save 8.8% to 82.5% total cooling energy consumption (TCEC), but increase the average percentage of dissatisfied during occupied hours (aPPD) from 7.5% to about 15%, which may cause overcooling penalty. The optimization decreases the thermal mass area and the night air change rate setpoint at each hour, while increases the minimum indoor air temperature setpoint compared to the general scheme. All three optimal NV schemes significantly improve the indoor thermal comfort by maintaining the aPPD at 7.5%. The optimal medium and high SFP mechanical NV scheme further save 7.1% and 38.6% TCEC compared to the corresponding general mechanical NV scheme, respectively. With a higher SFP, a greater energy saving potential is contributed through NV optimization process. Even though the optimal natural NV scheme consumes more than twice as much TCEC as the general natural NV scheme, it is still worth optimizing the natural NV since the indoor thermal comfort can be improved and the optimal scheme still saves much cooling energy compared to the base case. [ABSTRACT FROM AUTHOR]

• A methodology was represented for calibrating the building energy performance model. • Accurate calibration was achieved on the model even with the short-term measurement. • The calibration process was utilized the MBE- CVRMSE and obtained an enhanced accuracy. • The HDD and CDD was applied in the absence of the real weather data. • The accurate model also provides the opportunity to trail the systems' performance. The calibration process has an excellent advantage, especially for large-scale projects that manage the potentially complex energy performances of various building systems. It also provides great advantages when the annual real-time measurements are not available since the created model can be calibrated with short-term measurements to obtain an accurate annual energy performance. This paper presents a methodology for creating an energy performance model based on a calibration process of a large-scale residential building incorporating heating and cooling energy consumptions. The main purpose is to calibrate the building energy model by utilizing mean bias error (MBE) and coefficient of variation of root-mean-squared error (CVRMSE) indices and obtain the annual building-energy performance with enhanced accuracy. Furthermore, two novel approaches were developed. The first is to provide accurate weather data in the absence of real weather data by taking advantage of available data for heating degree days (HDDs) and cooling degree days (CDDs). The second is to provide accuracy on the collected data of measurements by taking advantage of the predicted data of short term measurements. A methodology is evolved to calibrate the building model by using real-time short-term monitored data from the building. The methodology consists of four main steps and further sub-steps. Modeling, the first step, covers the collection of input data and the development of the model stages. Measurements, the second step, covers the process of data collection for calibration. Improvements for calibration represent the third step, where different versions of the model are developed by changing the input parameters of the model within the defined ranges of variation (ROV). Then, the results of the obtained versions are compared with the real-time measurements, and MBE and CVRMSE values for each version calculated to examine the model's accuracy. Finally, when the model is considered as calibrated, the simulation is run to obtain the annual energy performance of the building for better precision. The results in the model run here show the monthly MBE and CVRMSE values for the initial version to be 15.12% and 17.19%, respectively. After applied strategies, these values are improved according to the calibration criteria defined by ASHRAE to −2.76% and 8.40% respectively, which is considered as calibrated. Overall, it is usually hard and costly to track the real-time energy consumptions of buildings for a long period. Annual building energy consumptions can be obtained accurately by calibrating the building model with the short-term measurements. An accurate model gives the opportunity to trial the performance of each building system. [ABSTRACT FROM AUTHOR]

In the context of residential energy consumption, the top-down approach and bottom-up approach have been developed to model and predict residential energy demand, and the bottom-up engineering model is a promising choice and an effective tool for urban planners and policymakers. However, unlike the existing building stock databases in some developed countries or regions, China's Pearl River Delta (PRD) region lacks such building databases. Thus, the study established a database of 120 residential districts in the PRD region, and developed typical models through the data of the collection, quantification and classification. In this paper, three typical residential district models have been defined, which was a supplement to previously develop seven typical residential building models. Moreover, four common family patterns in the PRD region were determined by literature research, the corresponding schedules and basic settings were developed subsequently for them. Finally, the simulations of the energy consumption of the three residential districts were conducted, whose energy consumption intensities were 28.43 kWh/m2a, 28.41 kWh/m2a and 28.24 kWh/m2a, respectively. The results of the simulation are similar to the results of the actual energy audit, which is considered that the typical residential buildings are representative yet simple-to-use energy model. [ABSTRACT FROM AUTHOR]

Nowadays, performing multi-objective optimizations of actual building designs is one of the most challenging problems of the building energy efficiency area. This paper aims to propose an efficient method to solve multi-objective optimization building performance problems using a novel metamodel-based approach. To this end, the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is dynamically coupled with artificial neural network (ANN) metamodels, which were previously trained with results of building performance simulations conducted using the EnergyPlusTM software. This new approach proposes an optimal way to generate the samples used to train and validate the ANN-based metamodels minimizing the total of building energy simulations necessary to train them, and guarantees accurate optimization results. To validate the strengths of the proposed method, it is applied to optimize the energy efficiency and thermal comfort of an actual dwelling in order to get the best trade-off (Pareto front) of the building between heating and cooling performance. This case study involves 12 of the more influential discrete and categorical design variables like roof types, external and internal wall types, solar orientation, solar absorptance, size and type of windows, and the dimension of external window shadings of this house among others, making a complex building performance optimization problem with more than 108 possibilities to choose. Furthermore, the results obtained are systematically compared and validated with the "true" Pareto front achieved using a simulate-based scheme which directly couples EnergyPlus program and NSGA-II algorithm. Results indicated that the presented method is able to reduce up to 75% the number of building energy simulations needed to find the Pareto front of an actual multi-objective building performance optimization problem, keeping a good accuracy of the results. [ABSTRACT FROM AUTHOR]

A strong correlation exists between occupant behaviour and space heating energy use. In particular, the occupancy status (e.g., daytime absence) is known to have a significant influence on residential heating load profiles, as well as on cumulative heating energy consumption. In the literature, many occupancy models have been utilised to predict occupancy profiles of individual dwellings as part of the larger residential building stock. However, none of the existing models consider diversity associated with occupancy-integrated archetypes to generate high-temporal resolution heating load profiles. The current paper uses Time Use Survey (TUS) data to develop a high-temporal resolution residential building occupancy model. The key feature of the proposed model, implemented using MATLAB, is the ability to generate stochastic occupancy time-series data for national population subgroups characterised by specific occupancy profiles. It is shown that the results are capable of closely approximating data available from TUS. The developed model can be applied to improve the quality of modelled high-temporal resolution heating load profiles for generic building stock characterised by population subgroups represented by different occupancy-integrated archetypes. A case study is performed on a building stock sample located in London, UK. The developed occupancy model has been implemented in MATLAB and is available for download. [ABSTRACT FROM AUTHOR]

• The results of an evaluation of the net zero energy performance of an office building in a hot and humid climate using measured data was presented. • The analysis methods applied to the building include: a measured energy use comparison; a change-point linear regression; and calibrated building energy simulations. • The results from the calibrated simulation that adjusted for differences in thermostat settings showed weather normalized savings of 45–50%. In the past 20 years, the U.S. Army has made significant progress in making their facilities more energy efficient. Currently, the Army is required by law to eliminate fossil fuel use in new and renovated facilities by 2030 and to reduce overall facility energy usage 30% by 2030. As part of this on-going energy efficiency effort, the U.S. Army has designed and constructed a Net Zero Energy Building (NZEB) at the Fort Hood army base in Texas, which is currently being tested as a prototype. In order to achieve net zero performance and reduce the energy demand, several low-energy building technologies were designed and built including high-efficiency heating, ventilation, and air conditioning (HVAC) system, a high-performance building envelope, energy efficient lighting and a solar PV system. This paper presents the results of an evaluation of the net zero energy performance of the office building at the Fort Hood army base using measured data. The analysis methods applied to the building include: an un-adjusted, measured energy use comparison of both portions of the building; a weather-normalized, change-point linear regression to estimate annual energy savings; and calibrated building energy simulations with thermostat schedules and occupancy conditions of both portions in the building. The results show savings of 37–50% for the renovated portion of the building (i.e., NZEB) compared to the un-renovated portion depending on the method of analysis. [ABSTRACT FROM AUTHOR]

• Evaluation of existing approaches or projects of building archetype. • Developing a generalized multi-scale building archetype methodology. • Implementing the data-driven approaches for the characterization of building archetypes. • Comparison of the modeling results of multi-scale building archetype at BEM and UBEM levels. Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings' physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions. [ABSTRACT FROM AUTHOR]