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Post-occupancy evaluation (POE) is necessary to evaluate the building's performance in order to formulate future design development after its utilisation. This paper evaluates indoor thermal performance, sound pressure level, and illuminance in a multipurpose auditorium by combining zoning and activity-based POE. The evaluation was done in three activities: (A) student graduation, (B) theater performance, and (C) public lecture with different types of clothing preferences. Three different zones were used to define the precise location of the building improvement. Field measurements were taken for thermal performance, including Operative Temperature (T op), Humidity (Rh), Sound Pressure Level (dB), and Illuminance (lux). User perceptions were collected from 171 respondents (89 men and 82 women). The field measurement showed that average operative temperature varied across the activities with 26.15–25.68 °C, 25.47–24.95 °C, and 23.74–23.17 °C on activities A, B, and C, respectively. Activity A's (1 clo) room's operative temperature showed that 65% of data are outside the ASHRAE standard, indicating that the room condition is unsuitable for 1 clo. Activity A's TSV shows a slightly warm sensation (0.21 to 0.93) and is still in the comfort range. With the same clothing value (0.6 clo), Activity B's T op indicates a more suitable condition than Activity C's. The average TSV in Activity B indicates slightly cool (-0.54 to −0.90), with the lowest TSV found in Activity C (-1.09 to −1.41). Although TSV positively correlates with PMV , Thermal Preference (TP) revealed that the occupant expects cooler conditions in all activities. Sound pressure level and illuminance performed well according to all activities. This paper recommends chiller setting adjustment in the multipurpose auditorium based on activity characteristics. Combined POE based on zoning and activity can indicate which aspects of the auditorium need to be improved. [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]

[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [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]

[Display omitted] • Building policies must be rooted in a good understanding of the targeted population. • Building performance standards may benefit from creating databases of weather files. • A thermal performance analysis should consider multiple key performance indicators. • Building performance policies should address the thermal resilience of buildings. This paper presents the main challenges faced and lessons learned when developing a new method to assess the thermal performance of residential buildings for the recently updated Brazilian building performance standard, NBR 15575–1:2021. It touches on subjects related to the representation of occupant behavior, climate, and thermal acceptability in building performance simulation, as well as the use of reference models, key performance indicators, and performance levels in policies. NBR 15575 provides a thorough procedure to analyze thermal performance, allowing buildings to be assessed while in passive operation mode and also accounting for energy needs when active operation is necessary. Multiple key performance indicators are introduced to provide a comprehensive evaluation. The main difficulty found was dealing with diversity. Given the vast scale of Brazil's territory, a high variation in culture, climate and construction techniques is expected, thus requiring a compromise between detail and scalability. There are still many opportunities for improvement, especially regarding the representation of occupants, and adjustments of the characteristics of reference models to promote high thermal performance, considering the diverse climates, regional practices, and economic needs. This paper might help researchers and other stakeholders to develop and improve other local standards and protocols, especially for warm climates. [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]

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth analysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants' feedback, a probabilistic model of occupants' comfort, and HVAC faults detection and prediction that may affect occupants' comfort. New methods for using BIM as a visualization platform, as well as a predictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants' comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integration among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunctions, and pointing toward possible solutions. [ABSTRACT FROM AUTHOR]

• Novel 24-h perturbation method for the estimation of the Heat Transfer Coefficient. • Binary signals with 5 + -hour heating and free-floating periods show highest accuracy. • BIC model selection among RC models most accurate for physical interpretation. • Proposed method very accurate in winter conditions. • Mid-season conditions might need slighlty longer measurement durations. On-site measurement of the actual thermal performance of a building envelope could considerably benefit to the building sector by securing an effective performance of newly built or retrofitted buildings. Current methods to estimate the Heat Transfer Coefficient, for example, suffer however from either excessive equipment downtime and therefore large costs, or fail to achieve a reasonable accuracy. This paper aims therefore at developing and assessing an original 24-h perturbation method for estimating a whole-building Heat Transfer Coefficient. Based on a binary heating signal, the proposed method exploits the data collected with stochastic RC models. The paper first presents a numerical comparison of 398 binary signals along with variable initial indoor temperatures to bring out common characteristics of signals that achieve highly accurate HTC estimations. An experimental campaign in a highly insulated wooden-framed house then implemented one particular signal and assessed its reproducibility under variable weather conditions. The paper shows that the proposed 24-h method indeed achieves a highly accurate estimation when performed in cold weather. In mid-season conditions, the method shows a better robustness from 30- to 36-h measurements. The results invite to further investigate other building types in variable weather conditions to validate the proposed method. [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 construction industry in the United States consumes more than 50% of the global energy supply per year, suggesting that significant efforts may be needed to reduce building energy demand and its carbon footprint. During their lifespans, buildings consume embodied energy (EE) and operational energy (OE). Building professionals, therefore, conduct life cycle energy assessments (LCEA) to quantify and understand the paradox and interconnectedness between EE and OE. Traditionally, simulation-based optimization techniques were used for design space exploration to identify a building design with the fewest energy implications. However, literature shows these data-driven approaches are often error-prone, time-consuming, and computationally expensive, and they fail to provide real-time feedback to the user. Moreover, EE and OE assessment tools are disjointed and suffer from interoperability issues. These limitations restrict design space exploration, which eventually hinders the design decision-making process. Over the last few years, the increased availability of building data has made machine learning (ML) techniques a popular choice for building performance assessments. Several articles have developed prediction models to assess or optimize OE. While this work is significant, studies utilizing ML techniques for building LCEA are lacking, mainly due to the unavailability of a large-scale LCEA database. In this paper, we propose a methodology to (1) generate a simulation-based building energy dataset for different building typologies using a parametric framework, (2) utilize the synthetically generated database to develop an ML-based prediction model to predict EE and OE, and (3) test the model using a case study. The case study results show that the model achieves high prediction performance using minimal inputs available during the early design phase. The results further indicate that ML techniques can be used by building designers with no or limited LCE expertise to instantaneously estimate building LCE performance and help them select design options with minimal LCE consumption. [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]

• The effect of the inaccurate load label on prediction models is analyzed. • The method of extracting load from cooling capacity is proposed. • Practical cases are used for validation. • The accuracy of ultra-short-term load prediction models is improved. Data-driven modeling achieves excellent performance in building load prediction by mining the actual load characteristic from operation data. When predicting room air-conditioning load, however, the measured data of cooling capacity cannot be used as the load label in the modeling as customary. The reason lies in the unstable automatic control of the air-conditioner, which makes the cooling capacity oscillate at the actual load. The mismatch of cooling capacity and actual load will affect prediction modeling. This paper first sets up a simulation experiment to quantitatively analyze this effect. The results indicate that the more the cooling capacity deviates from the load, the greater the error of the prediction model. To solve this problem, a load extraction method based on the Fourier decomposition is proposed, which involves transforming the actual cooling capacity data into the frequency domain and extracting the low-frequency components as load data, which is further used for modeling. The experiments show that the accuracy between extracted data and real load exceeds 90%. Additionally, the load extraction method improves the prediction accuracy by up to 17.55% compared to the model using cooling capacity data directly. Finally, a practical case demonstrates the effectiveness of the proposed load extraction method. [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]

In recent years, measurement protocols for the estimation of the total aggregate building heat transfer coefficient (HTC) have provided sufficient empirical evidence to indicate that buildings often do not perform as intended. However, little research has been carried out into the associated uncertainties. Within this context, this paper reviews sources of uncertainty associated with co-heating tests; characterises these uncertainties and their impact on HTC estimates; and devises a method for the calculation of HTC uncertainty. The method proposed was applied to 14 co-heating tests, showing estimated total uncertainty ranging between 2.2-21.1 ▪ (or 4.6-26.7% of the measured value) with a mean of 10.1 ▪ (or 8.7%). The natural variation of HTC and often-observed inaccuracy of design calculations (the 'prediction gap') suggest that more accurate measurements may be of little benefit. Additionally, results suggest that weather conditions, challenging building design and poor experimental technique can all significantly contribute to HTC uncertainty. However, when suitable buildings are tested by experienced technicians and under suitable weather conditions, HTC estimates from the co-heating protocol are likely to provide a useful tool to assess and understand real-world building fabric performance. • Uncertainties in steady state co-heating measurements are reviewed. • Uncertainties are characterised by their nature and impact upon HTC estimates. • A framework for estimating uncertainty in HTC estimates if given. • Range of estimated uncertainty on 14 tests is 5-27%. • Cases with poor experimental control and problematic test buildings result in higher uncertainty. [ABSTRACT FROM AUTHOR]

An excellent way to obtain a detailed description of the air flows and temperature field in a zone is to perform accurate Computational Fluid Dynamics (CFD) simulations. Unfortunately, this is often prohibitive due to the powerful resources that are required, and the large computational times. As a consequence, several intermediate models between CFD and the standard well-mixed models have been proposed over the years. These are known as zonal models, and they aim at delivering sufficiently accurate predictions of the thermal comfort in a room, for long periods (months or even years), and with substantially shorter computational times. However, none of these models has yet been implemented in a building performance simulation (BPS) tool. In this work, we have developed and implemented a BLOCK zonal model [1,2] within the IDA ICE (BPS) software. A multi-node 1D model for the prediction of vertical temperature gradients is used, together with flow element theory and an appropriate algorithm for the estimation of view-factors in non-convex geometries with obstacles. The paper presents the theoretical background of the model along with advice on its range of applicability. Finally, the model is validated against several test cases, showing its suitability in predicting the thermal stratification. [ABSTRACT FROM AUTHOR]

[Display omitted] • The application of OB models in the building design process is reviewed. • The role of OB models for a robust and resilient built environment is highlighted. • The gap between the occupant behaviour research field and the end-users is mapped. • A simulation framework for integrating OB models in the design practice is proposed. • Criteria for evaluating, choosing, and adapting OB models are given. Occupant behaviour is an important source of uncertainty in building energy performance simulations. This has led to the development and integration of different modelling approaches that represent the complex, stochastic nature of human-building interaction. Yet, several barriers prevent their wide use in simulation-aided building design. The procedures and practical solutions for integrating occupant behaviour models are segmented through the literature. Accordingly, this paper examines the state-of-the-art in the application of occupant behaviour models. Based on the PRISMA methodology, the literature is critically analysed to: i) identify and map the barriers between theory and application; ii) propose a simulation framework establishing the steps for integrating occupant behaviour models into building performance simulations; iii) synthetise practical solutions and highlight remaining challenges towards a simulation framework adequately integrating occupant behaviour. The paper stresses the added value within the decision-making process at different building design stages. Furthermore, key elements for identifying the appropriated modelling approach for each occupant behaviour aspect are presented considering factors such as type of behaviour, building type, and spatial and temporal scale. Ultimately, this critical review establishes guidelines for the integration of occupant behaviour into building design practice and defines a research pathway for bridging the gap between the OB research field and the simulation-aided building design practice. [ABSTRACT FROM AUTHOR]

[Display omitted] The hot box test method has been applied to evaluate both the steady-state (U-value) and dynamic thermal properties of building envelopes. However, the high construction cost of full-scale hot box apparatus and the testing time required (usually several days) may prevent its wider adoption. To overcome the limitations of full-scale hot box tests, this paper proposes a novel method to evaluate the dynamic thermal performance of building envelope components using a small-scale calibrated hot box and scaled-down specimen. In this paper, the scaling relationships of thermal properties evaluated using a full-size specimen and a scaled-down specimen are established based on the Laplace transform of the heat transfer equations. In addition, dynamic thermal properties obtained from scaled-down experimental tests are compared to the values calculated by the EN ISO13768 (ISO) method. A small-scale hot box with a 355 mm × 355 mm metering area was constructed and calibrated to validate the correlations. Three scaled-down concrete sandwich wall panels were then tested and the scaling relationship was cross-validated using the experimental results, finite difference (FD) simulations, and the ISO method. The results indicate that the dynamic thermal properties obtained from a scaled-down hot box test can be correlated to its full-size counterpart when certain conditions are met. The scaled-down hot box test is demonstrated to be an effective yet economical alternative to a full-scale test with significantly reduced experimentation cost and turn-around time. [ABSTRACT FROM AUTHOR]

• Incidence of windows design on energy and visual comfort is studied under climate change. • The case study is an office building located in representative European climates. • Triple low-emissive windows, in oceanic and continental climates achieves energy saving of −35 %. • Double selective windows assures savings from −27 % to −57 % in the warm and oceanic climate. • The primary energy will increase until 27 % in for warn climates considering the 2080 scenario. The design of the glazed elements of the building envelope is a critical issue for both new and refurbished building when the effect of climate changes and expected overheating conditions are considered. Indeed, the windows affect the building energy balance and also the natural lighting that impacts both on visual comfort and illuminance consumptions. This paper develops a methodological approach for performing a parametric analysis aimed at quantifying the effects of different window's designs on the total primary energy and visual comfort of an office in the most representative European climatic zones. The considered parameters are: windows' orientation, window to wall ratio, glass and frame type and the installation of shading system. A new approach for evaluating the resilience of the optimal solution selected for the current climates is introduced by taking into account medium and long-term emissions scenarios. This analysis suggests that in the warm-summer humid continental climate, a double selective window may be resilient. Indeed, in the worst emission scenario, the achievable annual energy saving will be around −26 % if compared with a clear double window. Meanwhile it will be lower than −10 % if compared with the same optimal solution but simulated in the short-term emission scenario. For the humid subtropical climates and hot-summer Mediterranean climates, the scientific community should be focused the development of new solutions because also a dynamic system as the electrochromic one, is not able to guaranteed energy saving in the future scenario. Indeed, also the optimal configuration selected under the current conditions would be characterized by higher primary energy request (between 16 % and 27 %) under the future emissions scenarios. [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]

• Spent coffee ground can efficiently be reused in GP-products manufacture. • Novel GP-products are suitable for innovative architectural applications. • Spent coffee ground based-GP products show superior energy saving behaviour. • The cost associated with GP-products production is lower to traditional ones. • A 19% energy improvement per year is achieved for the building envelope. This study investigates a possible alternative reuse of spent coffee grounds (SCG), the major residue of the brewing process, to manufacture green geopolymeric materials for innovative building applications in energy-saving construction, in line with the European Green Deal towards zero-energy building. Specimens were prepared by a combination of biomass fly ash from the Kraft paper-pulp process, as raw material (70 wt%), and SCG (up to 17.5 %), as filler. The high amount of reused bio-wastes makes the material fit the requirements for the Minimum Environmental Criteria (MEC) certification, in light of the Circular Economy (CE) approach. Sustainability is also boosted by the manufacturing process that completely occurred at ambient conditions (20 °C, 65 % RH). Materials engineering performance is evaluated to predict possible applications in construction and promote an integrated architectural design process to propose a "coffee-house" equipped with an innovative energy structure and envelope. For the scope, different technological solutions are designed and virtual energy modelling is implemented to simulate the performance of a building model in different climatic conditions and estimate the possible real efficacy of the proposed solutions in relation to building efficiency and cost management, as envisaged by the EU 2018/844 on the buildings' energy performance. The major result is that the developed material represents an optimum candidate to substitute traditional construction and building materials with a great manufacturing financial saving, up to 37 % for the 17.5 % SCG, and an energy improvement up to about 19 % per year, leading further saving in the yearly building management. [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]

[Display omitted] • Real-time identification of occupancy and window opening using AI-powered cameras. • Occupant-centric control strategy for better thermal comfort, energy efficiency and air quality. • The heating energy consumption was reduced by between 0.6 % and 29.0 %. • The level of indoor thermal comfort increased by up to 58.8 %. • Indoor CO 2 concentrations below 1000 ppm for 89.2 % of the time being occupied. Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air quality has received much less attention and investigation. This paper presented an Occupant-Centric Heating and Natural Ventilation Control (OCHNVC) strategy for enhancing indoor thermal comfort, building energy performance and indoor air quality. Firstly, real-time profiles of occupant behavior and window opening in a case study building were collected using artificial intelligence (AI)-powered cameras and deep vision algorithms. Secondly, shallow artificial-neural-networks predictive models were established for forecasting the responses of the studied building to different levels of occupant behavior and window opening behavior. Thirdly, an OCHNVC strategy tailored to the studied room was proposed and applied to the studied room. The strategy could lower heating energy consumption by between 0.6 % and 29.0 % and improve the level of indoor thermal comfort by between 0 % and 58.8 %, relative to a conventional control strategy. Moreover, the conventional window control strategy only maintained indoor CO 2 concentrations below 1000 ppm for 59.7 % of the period that occupants were within the studied room, while the proposed controller could do so for 89.2 % of the period. Future works shall focus on experimentally deploying the strategy to real buildings and evaluating its performance. [ABSTRACT FROM AUTHOR]

• A two-step novel method is proposed to create hourly building meta -models. • The most significant feature of this new meta -model method is sequential. • Ensemble machine learning models are recommended to create building meta -models. • Distributed lag machine learning models are fit for hourly building energy models. The meta -models have been widely used to replace computationally expensive engineering-based building energy models for model calibration, sensitivity analysis, and performance optimization of buildings. However, most meta -models are used for monthly or annual building energy analysis applying the same procedure as building energy or load prediction. A few studies thoroughly explore the characteristics of meta -models when creating hourly machine learning-based meta -models for building energy assessment, which is very different from conventional building load or energy prediction. Therefore, this paper proposes a new method of creating time series models for building energy analysis based on machine learning techniques. The main feature of this new method is to include two steps (model preselection and data folding) by taking advantage of the sequential nature during the process of constructing meta -models. A case study of office buildings is used to demonstrate the application of this new approach using the EnergyPlus program and R computational environment. The results indicate that the time-series meta -models constructed using this new method have very high accuracy with a moderate computational cost. For the best models, the R2 values are above 0.99 and the CV(RMSE) (coefficient variation of root mean square error) values are below 0.06 for both heating and cooling energy prediction. The ensemble machine learning models, including Cubist, gradient boosting machine, and stacking, are recommended to create the meta -models for hourly building energy analysis. The distributed lag models combined with these ensemble models can be used to utilize the time-series features of hourly building energy assessment. The method proposed here can be extended to the time-series energy analysis in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind). [ABSTRACT FROM AUTHOR]

This paper presents a numerical investigation of dew formation on the roofs of buildings, and its influence on building thermal and energy performance. A new semi-analytical model was developed to evaluate the effects of rooftop dew, including the release/absorption of latent heat due to condensation/evaporation, the variation of apparent thermal emittance of the roof upper surface, and the dew runoff characterised based on laboratory experiments. A set of case study simulations was carried out by implementing the rooftop dew model in the building performance simulation (BPS) software EnergyPlus. Through its impact on roof top surface temperature, dew was found to significantly influence predictions of benefits and penalties of a 'cool roof' versus a bare metal roof. When dew effects were neglected in the simulations, the electricity savings attributed to the cool roof were over-predicted by between 39 % and 204 %, however, the gas heating 'penalty' of using a cool roof was over-predicted by 266 % to 362 %. Results from this study demonstrate that the thermal effects of dew on roofs can be important in BPS studies, and the model that is presented provides a relatively simple means by which to include such effects in simulations. [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]

• A study on housing stock improvement potential in Mediterranean areas is presented. • Three typical case studies were chosen from the stock of a southern Spanish city. • Monitoring on environmental and electricity data and tests logged real performance. • Energy models were built and adjusted based on real occupancy conditions. • The considered building retrofits raise indoor temperature and reduce discomfort. Renovating buildings is one of the major targets within the current European goals toward decarbonized cities in 2050. The residential stock offers extensive opportunities to achieve current energy horizons, since more than 75% of the existing buildings in the European Union are energy deficient, with poorly insulated thermal envelopes which drive indoor conditions far from current comfort standards. This paper aims to provide a protocol for forecasting the upgrading potential of this housing stock in southern Europe, particularly in Mediterranean areas, identifying the level of obsolescence and possibilities for improvement, especially in winter indoor thermal conditions, offered by standard passive strategies. Following an architectural, constructive, and energy analysis of common patterns throughout the urban stock of the southern Spanish city of Córdoba, three case studies built-in 1951–1980 – pre-dating the first Spanish regulations on thermal conditioning in buildings – were selected for the assessment. Long-term monitoring and in situ tests (i.e., infrared thermography, airtightness) recorded the performance of these buildings under real occupancy conditions, subsequently incorporating them into the calibration of simulation energy models, aiming to predict the potential improvements after implementing standard passive strategies in these buildings. Results show that building retrofits not only improve the quality of indoor thermal conditions by increasing indoor temperatures by 3–4 °C but also almost eliminate occupied hours outside the comfort range. [ABSTRACT FROM AUTHOR]

• Embodied and operational carbon tradeoffs in building envelopes are studied. • Envelope variables include wall assemblies, WWRs, glazing and shading. • Energy decarbonization models are used to determine the 30-year operational carbon. • Results show the importance of a total carbon approach to envelope design. • Over or under insulation can result in waste of 10–150 kgCO 2 e/m2. The building envelope has a substantial influence on a building's life cycle operational and embodied carbon emissions. Window-to-wall ratios, wall assemblies, shading and glazing types, have been shown to have a significant impact on total emissions. This paper provides building designers, owners, and policy makers with actionable guidance and a prioritization framework for establishing co-optimized lifecycle carbon performance of facade assembly components in a broad spectrum of climate contexts and energy carbon intensities. A large parametric study of building envelopes is conducted using building performance simulation and cradle-to-gate embodied carbon calculations in 6 US cities. The authors derive the total carbon emissions optimization for commercial office and residential space types using standard code-reference models and open-source lifecycle data. Comparisons between optimal total carbon solutions and (i) optimal operational carbon and (ii) minimum required assemblies, show the impact of under and over investing in envelope-related efficiency measures for each climate. Results show how the relationship between embodied and operational carbon is highly localized, that optimal design variables can vary significantly. In low carbon intensity energy grids, over investment in envelope embodied carbon can exceed as 10 kgCO 2 e/m2, while under investment in high carbon intensity grids can be higher than 150 kgCO 2 e/m2. [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]

A rapidly warming planet provides a significant challenge to the design, delivery, and maintenance of habitable housing across Australia. Information on the adequacy of current measures for cooling Indigenous houses in regional and remote communities in relation to climate change and rising energy consumption costs is lacking. Such information is needed to better protect low socio-economic status householders, people with pre-existing health conditions, and people living in climatically exposed regions. This article models the thermal performance of both existing and 'improved' (built to current national construction code standards) housing stock in relation to various occupancy density, cooling system, and ventilation practice scenarios. This modelling was undertaken for housing in Australia's tropical, arid, and hot/mild climate zones, matching the sites where most discrete Indigenous communities are located. Results show that retrofitting existing housing stock and even building new stock to meet recommended standards are not adequate measures for current or future climate changes. This paper challenges current knowledge, policy, and practices regarding Indigenous housing, demonstrating that significant reform is necessary to maintain habitable conditions and improve energy resilience. [ABSTRACT FROM AUTHOR]

Heating and cooling of residential buildings is accountable for a significant share of the EU's final energy consumption. In order to determine the current condition of the building stock and to implement appropriate measures and requirements to improve energy efficiency of buildings, a detailed analysis of existing buildings is essential. This paper aims at describing characteristics of the building stock of Salzburg by analysing energy performance certificates. Therefore, the building stock is grouped into archetypes based on the construction period, building condition (not-retrofitted and retrofitted) and building type (single-family house and multi-family house) and then analysed regarding energy need for space heating, energy carrier, heat emission system, share and U-values of building elements and window to wall ratio. The analysis of the data demonstrates that the structure and naming conventions within the EPC-database pose hurdles for a correct investigation of the data. A comprehensible and distinct notation as well as versioning is inevitable for the sake of avoiding a loss of information and enabling international comparability. An analysis of energy carriers of the archetypes reveals, that a significant share of buildings still uses fossil fuels like oil and gas for energy supply, although electricity (heat pumps) and district heating are on the rise for buildings constructed in recent periods as well as for retrofitted buildings, with heat pumps being the most important energy carrier for new single-family houses amounting to a share of 65 % and district heating for new multi-family houses amounting to a share of 46 %. A detailed observation of the thermal performance of buildings and building elements reveals high potential and practicability not only for energy efficient new buildings, but also retrofitted buildings throughout all construction periods and for both, single-family houses and multi-family houses (e.g. median U-values for retrofitted ceilings/roofs range from 0.17 to 0.26 W/(m2K) compared to a median of 0.14 W/(m2K) for not-retrofitted ceilings/roofs from the most recent construction period). The conducted analysis provides detailed statistical information on the Salzburg building stock and hence a robust basis for further studies regarding the thermal performance of the building stock and future scenarios by means of urban building energy modelling approaches. Furthermore, based on this information, policies for energy efficient buildings can be improved and adapted according to current circumstances. [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]

This paper models scenarios for energy label based retrofit targets for Switzerland's residential building sector for 2050. It takes into account the magnitude and uncertainty in the energy performance gap (EPG) between labelled and actual energy consumption using actual energy performance data, as previous work highlighted that the EPG is not evenly distributed across different building labels. A building stock retrofit model is developed to estimate the potential energy savings from improving building performance through increasing energy label rating. A Monte Carlo analysis is applied using real EPG distribution data. The uncertainty in total energy savings is calculated for business-as-usual case and for three scenarios based on the different retrofit incentive policies of different Swiss Cantons. This allows to quantify the uncertainties in energy and emission outcomes of different policy targets arising from the variable EPG. The results show that with the business-as-usual approach it is impossible to achieve the objectives set (9.7 Mt CO 2 -eq/y vs 0.4 Mt CO 2 -eq/y by 2050). On the other hand, to reach the targets, a more aggressive policy aiming at renovating all buildings to the highest standards is necessary, that translates into a renovation rate that seems unrealistically high (3.4% per year until 2050 vs the proposed 1.4%). The contribution is to inform stakeholders designing new policies (notably the EU Renovation Wave policies) of the risk related to energy policies. [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]

Reliable building simulation models are key to optimizing building performance and reducing greenhouse gas emissions. Informed decision making requires simulation models to be accurate, extrapolatable, and interpretable, all of which require calibrating model simulations to ground truth. Complicated building dynamics and highly uncertain exogenous disturbances make the model calibration process challenging and expensive; hence, a scalable and efficient calibration approach is needed to enable actual application. Current automatic calibration algorithms do not leverage data collected from multiple sources: for example, data obtained from previous calibration tasks on other buildings. In this paper, we employ probabilistic deep learning to meta-learn a distribution using multi-source data acquired during previous calibration. Subsequently, the meta-learned Bayesian optimizer accelerates calibration of new, unseen tasks. The few-shot (that is, requiring few model simulations) nature of the proposed algorithm is demonstrated on a Modelica library of residential buildings validated by the United States Department of Energy (USDoE). The proposed algorithm is compared against classical Bayesian optimization-based calibration, and it is shown that ANP significantly sped up the calibration procedure: the optimal model parameters are identified with 40–60% less simulations compared to the baseline. [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]

• Develop a new strategy to evaluate GAN accuracy for building power demand prediction. • Identify and evaluate five GANs for predicting large-scale building power demand. • Recommend Original GAN and cGAN for large-scale building power demand prediction. As an unsupervised-learning data-driven model, Generative Adversarial Networks (GANs) have recently attracted a lot of attention for various applications. There is potential to apply GANs for large-scale building power demand prediction, which is needed for power grid operation. However, there are many GAN variations and it is unclear which GAN is suitable for this application. To answer this question, this paper identifies five promising GANs (Original GAN, cGAN, SGAN, InfoGAN, and ACGAN) and evaluates their performance for predicting building power demand at a large scale. Physics-based building energy models are developed to generate training and reference data. A new evaluation indicator that combines accuracy and reproducibility is proposed to evaluate the performance of different GANs in predicting building power demand. The results show that SGAN and InfoGAN are not suitable because they cannot control the number of generated building samples for different building types. The prediction performance among the Original GAN, cGAN, and ACGAN can vary depending on training sample sizes and number of building types. If the training sample size is sufficiently large, Original GAN and cGAN can predict building power demand more accurately than ACGAN with the same number of samples. If training samples are limited, Original GAN provides better accuracy than cGAN and ACGAN. When the number of building types increase, the prediction accuracy increases for cGAN, decreases for ACGAN, and remains the same for Original GAN. As a result, cGAN and Original GAN are recommended for large-scale building power demand prediction. [ABSTRACT FROM AUTHOR]

• Building energy performance is negatively impacted by suboptimal operations. • Data-driven operations analytics may aid an operator's decision-making processes. • Insights were derived using data from interviewees' individual building portfolio. • Operators were hesitant in addressing deficiencies, specifically controls changes. • Possible barriers to effective utilization of data-driven insights were identified. Data-driven operation and maintenance analytics for improving building energy performance are prevalent in the literature and produce various visualizations and key performance indicators that identify hard and soft heating, ventilation, and air conditioning faults, occupancy patterns, and energy use deficiencies. However, an underlying assumption exists that the outputs of these analyses, some adopted from ASHRAE guidelines, can effectively impart actionable energy-saving insights to building operations personnel and augment the handling of faults, optimized operating controls, or building-level scheduling. This paper attempts to identify potential barriers to effective interpretation and utilization of data-driven, energy-saving insights by building operators and facility managers. Eleven operations professionals were interviewed and assessed regarding what initiatives or changes they would make in light of various outputs of multiple established data-driven approaches. Data from the interviewees' individual building portfolio were inputted into a multi-source data analytics web application, employing fault detection and diagnosis and inverse modelling techniques, and its outputs were presented to interviewees. Only two of the 11 interviewees expressed interest in making controls changes in response, revealing a possible knowledge gap of data-driven analytics, combined with the risk-adverse nature of building operations, which may inhibit operations personnel from effectively utilizing data-driven insights as presented. [ABSTRACT FROM AUTHOR]

• A model-based integrated envelope performance evaluation method is proposed. • Model with paralleled internal mass is recommended for Nearly Zero Energy Building. • The proposed model could keep robustness under of internal heat gain uncertainty. • The evaluation was demonstrated by a Nearly Zero Energy Building renovation case. The thermal transmittance plays a decisive role in building energy efficiency design, which determined by standard in-situ measurement are obviously not equivalent to the practical building load level. There is still a lack of post-evaluation method that can separately evaluate the energy saving effect provided by integrated building envelope, especially for the nearly-zero energy building (NZEB) whose thermal inertia cannot be neglected. This paper proposed a model-based integrated building envelope performance evaluation method, which contains a reverse parameters identification and a forward load calculation. The data-driven lumped-parameter model represented by resistance–capacitance network (RC-model) reflects the integrated envelope performance, and four different model structures were designed for different treatment of thermal mass were compared. The applicability of the selected 7R6C model in the simulation of NZEB is demonstrated by the accuracy verification, sensitivity analysis of initial values of temperature node and uncertainty assessment of internal heat gain. A renovation case achieved an annual heating demand assessment result of 14.0 kWh/(m2·a) under typical annual meteorological data, which is lower than the limit value of a NZEB in cold region of 15.0 kWh/(m2·a). The evaluation results show that the envelope renovation meets the requirements of energy efficiency design. On the way to low-carbon buildings, the corresponding post-evaluation method needs to be gradually improved. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proposed method can be applied to all window types and does not require the manual adjustment of threshold values according to different weather conditions. • The prediction accuracy of the proposed method in the case study reaches 97.79%. • The features of window opening behavior for a teaching building in severe cold regions are explored. • Occupants of teaching buildings in severe cold regions in the transition season prefer a large opening angle (45°∼180°). The building industry plays an important role in the effort to achieve carbon neutrality, and window opening behavior is a major contributor to building energy consumption. In severe cold regions, opening windows during the heating period causes energy waste. If the window opening behavior can be monitored in real time, then the occupants' energy use behavior can be guided to reduce energy waste. This paper proposes a window opening behavior monitoring method using a deep learning-based image recognition model that can identify the window state based on real-time video and was validated in a case study based on outdoor thermal and wind data. A teaching building with casement windows was used as a case study. The results showed that the occupants preferred a large window opening angle (45°∼180°), which is a choice that significantly affects the building energy consumption. The prediction accuracy of the proposed method can reach 97.79%, which also serves to avoid the difficulty of manually calibrating the pixel threshold and realize real-time feedback. This method has a wide range of application scenarios that enable us to obtain window opening behavior to improve the building performance simulation accuracy and guide occupants' energy consumption behavior. [ABSTRACT FROM AUTHOR]

• An elastic weight consolidation-based method is proposed for building load prediction. • The proposed method has higher accuracy than conventional prediction methods. • The proposed method costs low computational and storage source. • The proposed method has higher model stability than sliding window fining-tuning. Data-driven building energy load prediction models should be updated dynamically to adapt to building performance degradation and changes of outdoor environment. Conventional dynamic modeling methods usually have to either increase the costs of model training and data storage for obtaining high accuracy, or decrease accuracy for reducing the costs of model training and data storage. Therefore, this paper presented an elastic weight consolidation-based sliding window fine-tuning method for dynamic building energy load prediction modeling with high accuracy, low computational costs, and low data storage costs. It adopted elastic weight consolidation to make a model be able to remember historical knowledge and learn new knowledge without storing historical data. Three common dynamic modeling methods (sliding window retraining, accumulative retraining, and sliding window fine-tuning) and a static modeling method were adopted for performance comparisons with the proposed method using the five-year operational data of a public building. According to the results, the mean absolute error of the proposed method decreased by 66.58%, 9.06%, and 8.70% on average compared with sliding window retraining, sliding window fine-tuning, and static modeling, respectively. It was further demonstrated that the proposed method had better stability than sliding window fine-tuning. Moreover, the proposed method showed similar accuracy to accumulative retraining, while it had lower costs of model training and data storage than accumulative retraining. [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]

• The resilience of a building designed to be nearly zero energy is evaluated in Mediterranean climate. • Weather file defined with monitored data are used for a short-term analysis. • Projections of climate changes are considered in the construction of weather files. • The net primary energy will decrease (–23%) and the in-situ renewable adoption will increase (85%). • Overheating will occur in the intermediate months with the need to turned on the cooling system. The paper is focused on the evaluation of how the climate changes can influence the performance of buildings designed to be nearly zero energy (nZEB) and on the evaluation of the resilience in term of energy balance. A real case study is used; it is named BNZEB, a single-storey dwelling built in Benevento (South Italy, Mediterranean climate). With five-years monitored meteorological data, both a short-term and medium-term analysis is developed; the first one consists in the evaluation of the energy behavior compared to the first operating year. Then, the energy performance is evaluated considering the medium-term climate projections generated using the CCWorldWeatherGen tool. This analysis, with reference to the case study, suggests to designers and researchers that, in a typical Mediterranean climate, the reduction of the heating demand could compensate the increment in the cooling request. In the worst climatic scenario, the net primary energy could change from 25.4 kWh/m2 year (first year of life, 2017) to 19.5 kWh/m2 year (projection to 2050) with a rate of self-consumption of 85% (80% for 2017). However, a critical point is the operating period of the cooling system. Indeed, an indoor overheating problem could occur during the spring with operative temperature higher than 30 °C; this indicates that in future, the occupants will require higher number of operating hours with most frequent switch off. This could change the results of the energy balance, reducing, for instance, the imbalance between production and consumption that currently occurs in the intermediate periods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Environmental performance is assessed for low-energy/passive house dwellings. • The current and future Northern Ireland electricity mix scenarios are analyzed. • Employing passive-house standards could reduce the building's environmental impact. • The electricity decarbonization leads to a significant reduction of GWP. • Further environmental benefits can be ensured by focusing on the material market. This paper implements a Life Cycle Assessment of several low-energy/passive house buildings located in Northern Ireland. This work aims (i) to assess the environmental performance of the buildings and (ii) to evaluate the effect of electricity decarbonization on the dwellings' global warming potential (GWP). Three different future electricity mix scenarios have been used and compared to a static scenario where the current electricity mix remains constant. The LCA results of the static scenario reveal that applying passive-house standards could reduce the impact of dwellings while it does not necessarily provide a positive environmental outcome. The building operation phase contributed the most to the environmental impact, while negligible impact comes from the end-of-life stage. The electricity decarbonization leads to a significant reduction of GWP in all cases, with the highest value achieved for the passive house using the highest share of electricity, 58%-70% GWP reduction compared to the static scenario. Moreover, electricity decarbonization increases the relative share of the production stage to the overall building emission. Therefore, close attention should be paid to construction material selection in any effort aiming to achieve further environmental benefits. The buildings' environmental and operational energy performances were also compared to the RIBA 2030 Climate Challenge. [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]

Monitoring buildings' heat loss and quickly discovering high energy-consuming buildings in a certain area is of great significance for urban management departments to propose urban governance and management measures from the perspective of global optimization. Developing a 3D-infrared (3D-IR) model of a building or a complex created with infrared thermograph (IRT) obtained by unmanned aerial vehicle (UAV) technologies would greatly contribute to a comprehensive observation on the thermal performance of a study objective. In this paper, a procedure for high energy-consuming building screen based on the 3D infrared (IR) models of multiple buildings is proposed and demonstrated as an effective approach for regional evaluation comparing with measured heating energy data provided by the local municipal department. Firstly, the 3D-IR model of multiple buildings is established, where the single building models are built based on UAV-IRT technology respectively, and the modelling condition as well as the temperature-color mapping relationship of each building is unified. Secondly, the measuring schemes and the evaluation method of the building thermal performance based on the 3D-IR model is proposed. Thirdly, the energy-consuming performance of five buildings in Jining, a city in northern China, is measured and analyzed by using the above-mentioned methods. Finally, the proposed evaluation method of the building performance is applied to evaluate the tested buildings. By referencing to measured heat energy data, the proposed method can be taken as an efficient and reasonably accurate screen method. [ABSTRACT FROM AUTHOR]