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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]

• 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 • Most of the existing calculation methods are outdated and do not fit climate-proof buildings.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]

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

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

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