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• HVAC system stock is heterogeneous and changes over time. • A HVAC stock modeling and an archetype building energy modeling were integrated. • Disregard of the heterogeneity and its change can cause error in energy modeling. • Energy reduction by technological innovation relies on the share of the HVAC stock. Urban building energy model (UBEM) is an important tool in coping with energy issues of building sector. This paper focuses on an issue that the stock of heating, ventilation, and air-conditioning (HVAC) systems has been oversimplified in UBEM without considering its heterogeneity. It has been observed that this oversimplification may incur a significant error in the result. To address this issue, this paper proposes a framework that integrates a logistic regression based HVAC system stock modeling method and the bottom-up type engineering modeling method using building archetypes considering HVAC system configurations. A case study with Japanese office building stock revealed that (1) the disregard of the heterogeneity resulted in an error of 15% in the annual primary energy consumption, (2) the historical change in the HVAC system stock could decrease the consumption up to 5%, and (3) its composition has a significant influence on the energy saving due to energy efficiency increase in heat source machines used in HVAC systems. The presented method would contribute not only to improving the reliability of UBEM but also to providing an analytical capability of considering energy efficiency technologies related to HVAC systems along with the alternation of technology selection in the building stock. [ABSTRACT FROM AUTHOR]

Occupant energy demand plays an important role in whole building energy consumption. To improve building energy efficiency, the stochastic characteristics of occupant behavior should be explored. In this paper, an occupant-based energy consumption prediction model was proposed based on the analysis of the relationship between occupant behavior and equipment energy consumption, drawing from an indoor occupancy rate model and computer input power model. Polynomial and Markov chain–Monte Carlo methods were applied to describe the time-varying indoor occupancy rate and the computer input power in multi-occupant office rooms. The computer energy consumption and occupant activity were related through the time-varying indoor occupancy rate. The energy consumption of office equipment was calculated by time accumulation and necessary correction. Three office buildings with different functions were selected as case studies, which are mainly used for business, administration and scientific research. The error rate between the predicted energy consumption from the model and actual energy consumption record was below 5%. [ABSTRACT FROM AUTHOR]

The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. Estimates suggest that between 5% and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected faults in air handling units (AHU) that cause large building energy inefficiencies. Algorithm for faults like stuck dampers and leaking dampers have been developed and tested in this particular case study. These damper fault detection algorithms can be applied to outdoor air and return air dampers both. The technical contributions of this work include expert rules that adapt to the Heating, Ventilation and Air-Conditioning (HVAC) equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. The combination of expert-rule based fault detection combined with first principles thermodynamic modeling is a unique contribution of this work that leads to quicker fault detection with minimal non-intrusive measurements. These contributions are particularly unique in their treatment of models and the careful consideration of user-interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as stuck dampers and imbalanced airflows within several large air-handling units were targeted. The algorithms focused on detecting faults with minimal data and non-intrusive measurements. An example is presented of the potential energy savings in a large academic building that has been monitored. Real data from an academic building in Boston was collected from the building energy management system (BEMS) for the purpose of this study. Savings of around $3400/month or 18% of the monthly cost when a stuck damper fault occurred over an entire month in an air handler. Another fault that was investigated was that of leaking dampers which lead to savings of approximately $500/month or around 2.5% of the monthly energy expense. The savings would accrue if the fault were corrected; otherwise the occurrence of the fault causes a waste of money and energy predicted. The algorithms developed can be applied to large commercial office buildings, academic buildings, hospitals and other modern buildings in various climates as long as they have data collecting capabilities. [ABSTRACT FROM AUTHOR]

Access to modern energy services in households is central to achieving decent living standards and well-being. Almost 3 billion people in Sub-Saharan Africa and developing Asia still lack access to modern energy services and endure energy consumption rates equivalent to a fraction of that in developed countries. While critical for the successful achievement of several internationally agreed Sustainable Development Goals (SDGs), closing this energy divide may pose an additional pressure on the already daunting challenge of securing climate stabilization. The ways in which this divide is closed will significantly affect the development of buildings-related emissions. This paper revisits previous work on the potential conflict between efforts towards closing the energy divide and enabling the achievement of higher levels of human well-being, and those associated with climate stabilization. It estimates the additional energy needed to achieve the former in terms of final energy consumption levels in key regions, and the associated emission pathways under different climate action scenarios. It then analyses the impact that such pathways could have on estimated carbon budgets associated with fulfilling the Paris Agreement, and provides some suggestions on how this impact could be minimized in the buildings sector. [ABSTRACT FROM AUTHOR]

Although smart thermostats have increasingly provided homeowners with abundant operational data related to advanced HVAC control and energy usage management in homes, there is a lack of systematic frameworks that can utilize such data to generate actionable information for advanced home comfort system diagnosis and control. Recognizing that a home thermal model, which is capable of connecting weather and HVAC operational data, is crucial to this framework, this paper introduces a home thermal model that is built upon the standard RC (Resistor–Capacitor) approach and the one virtual envelope assumption to describe the thermal dynamics of a home. A parameter estimation scheme is also developed that enables automatic, sequential, and optimal estimation of the model parameters, i.e., the thermal properties, of a home. Finally, the home thermal model and its parameter estimation scheme are tested using data collected over 63 consecutive days from a test home. In the test, the length of data that can provide enough robustness for parameter estimation is investigated. It is found that 10 days of data are enough, although 20 or more days of data can provide more robust results. In addition, when the model is used to make 12-hour-ahead predictions of indoor temperature, the resulting mean, maximum, and 95%-confidence-interval absolute are 0.31°C, 1.72°C, and 0.90°C, respectively, if the model is trained using 10 consecutive days of data, and 0.27°C, 1.19°C, and 0.72°C, respectively, if 20 consecutive days are used. Therefore, the proposed home thermal model requires only a modest amount of training data to yield fairly accurate prediction and is able to perform better with more data. [ABSTRACT FROM AUTHOR]

Due to the rapid development of Advanced Metering Infrastructure (AMI), data-driven approaches are becoming more effective and accurate for Measurement and Verification (M&V) baseline energy modeling of existing buildings using historical energy consumption data. This paper develops and applies a comprehensive analysis framework suitable for developing baseline data-driven models to estimate energy savings from building retrofits. The framework employs common data-driven modeling approaches for building energy prediction, necessary data processing tasks, and industry-used evaluation methods. The modeling approaches considered by the developed framework include linear regression, ensemble models, support vector regression, neural networks, and kernel regression. The developed framework is applied to two case studies with different sources of energy consumption data generated via a simulated office building model and actual measurements obtained from a publicly available dataset. The framework applications to data from 170 buildings with 1-year data result in energy use predictions with medians of Goodness-of-Fit (GOF) scores that are less than 25%. • A data-driven framework is proposed to automate baseline modeling for measurement and verification analysis. • The framework considers several data-driven modeling approaches and timestamp frequencies. • The framework is able to predict within 5.54%, the energy savings incurred by retrofitting an office building. [ABSTRACT FROM AUTHOR]

• The data requirements of quota methods are summarized, and a city-scale database containing 670 credible government office buildings is established. • By comparing the processes, the results and applicability, the selection basis of quota methods under different conditions is refined. • A flow chart of the formulation process and method selection of city-level building energy consumption quotas is constructed. • It is proposed that special attention should be paid to the outliers, and the characteristics of outliers in Chongqing are summarized. Energy consumption quota policies are effective management measures to promote energy conservation and emission reduction in China. Cities need to choose an appropriate method to formulate local energy consumption quota standards. This paper is based on the data of 670 government office buildings in Chongqing and the selected quantile method (QM), quota-level method (QLM) and multiple linear regression (MLR) method, which are widely used in the formulation of city-level energy quotas. By comparing their interpretability, evaluation, consistency and stability, the application conditions and applicability of each method are summarized. MLR has the strongest explanatory power but the highest requirements for the technology and information dimensions. The QLM is more explanatory and stable than the QM and is more suitable for the initial stage of energy quota management. When using the QLM, we should consider scientific classifications and reasonable quota levels. Special attention should be given to "outliers" with abnormal evaluations of the quota results. The outliers in Chongqing are characterized as buildings that have been used for more than 10 years and have an area of less than 5000 m2. Finally, this paper constructed a flow chart of the formulation process and method selection to help policymakers better understand how to choose the most appropriate method and identify inefficient buildings. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the energy usage pattern of office building all year round using time-series energy consumption data. This investigated pattern could uncover the energy utilization issue availing building energy efficiency implementation. Past researches principally focused on the total energy condition instead of time-series in terms of time cycle. This paper implements the innovative artificial intelligent algorithms to perform the data mining target via cluster analysis and association rule discovery between different types of energy. Official energy building models provide the studied database. The result shows that k-shape and apriori algorithm could successfully obtain the energy using pattern hidden dataset. There is significant dispensable energy wastage after working time with office building since the long leave course happening after 18:00. The main subentry energy determining total energy is different in disparate stages. Moreover, cooling energy primarily manipulates the total energy in most of time indicating more than 80% degree in terms of confidence level. Conclusion illustrates that this workflow could successfully detect power load profile features and find the unreasonable issues with energy using. Combining above cluster and association analysis outcome contributes to the energy adjustment in each period more precision. [ABSTRACT FROM AUTHOR]

Plug loads are one of the most important energy consumption items in non-residential buildings and their weight is continuously increasing in new generation buildings with highly insulated envelopes and high efficiency HVAC systems. An accurate and realistic modeling of plug loads is therefore of paramount importance for building energy modeling and energy efficient operation, as well as for other emerging applications such as renewables integration and demand response. We present in the present paper a method for generating aggregated electric load profiles for plug loads in office buildings based on more than 6400 field-measured load profiles of individual appliances used in office buildings and a field survey covering more than 1000 office building occupants which provides the distribution of the considered appliances in office buildings. We show that the aggregated load curves scale up following different laws depending on the considered appliances. We also provide aggregated load curve stabilization thresholds for all appliances: 43 for desktop computers, 32 for computer screens, 127 for laptops and 204 for multifunction devices if we mention the main office appliances. Finally, by combining the individual appliances' aggregated load curves and the survey results, we propose a model for generating building-level aggregated plug load curves for three main building types defined through the survey data analysis. We show that the aggregated load curves scale up differently when the number of occupants increases depending on the building type and the day type. Obtained results provide unique insights on plug load aggregated load profile shapes and behaviors and can be very useful for researchers and engineers interested in non-residential building energy modeling. [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]

The focus of carbon management is shifting from operational carbon to embodied carbon as a result of the improved operational energy efficiency of buildings. Measuring and managing embodied carbon right from the early stages of projects will unlock a range of opportunities to achieve the highest possible emissions reduction which could not be achieved otherwise during the latter stages. However, measuring embodied carbon during the early stages of design is challenging and highly uncertain due to the unavailability of detailed design information. Therefore, the research presented in this paper addresses this problem in a structured and an objective way. A parametric embodied carbon prediction model was developed using regression analysis to estimate embodied carbon when only minimal design information is available and with less uncertainty. The model was developed by collecting historical data of office buildings in the UK from four different data sources and estimating embodied carbon by combining several estimating techniques. Wall to floor ratio and the number of basements were identified as the model predictors with a model fit of 48.1% (R 2 ). A five-fold cross-validation ensured that the model predicts within the acceptable accuracy range for new data. The developed model had an accuracy of ±89.35% which is within the acceptable accuracy range for an early stage prediction model. In addition, the need for standardising embodied carbon measurements and to develop embodied carbon benchmarks to facilitate embodied carbon estimating throughout the project lifecycle were emphasised. [ABSTRACT FROM AUTHOR]

Thermal properties have significant impact on ecological footprint and life-cycle assessment of buildings. This is even more crucial aspect for glass buildings, which have been criticised as major factors in climate change around the world. Recent debates on glass facades, which culminated in plans for a "glass building ban" in New York City highlighted the importance of innovation in glass construction. The current solutions for mitigating energy consumption for glass facades are improving insulation (U-value) and solar heat gain coefficient (SHGC) with advanced glazing or using shading devices. A fourth approach is the recent technology of water-filled glass (WFG), which utilizes water layer to improve thermal comfort and energy performance of the façade. This paper introduces Smart Water-filled Glass (SWFG) control method, which enables the change of the opacity of the façade element by colouring the fluid over a year regarding seasonal changes. The impact of changing transparency of water layer on cooling and heating energy demand is evaluated by simulating and analysing the energy performance of a reference office room with large glazing facade in different climates. The significance of this energy evaluation is that a water-filled glass with switchable transparency is simulated and assessed here for the first time. The paper analyses the performance in seven cities from all relevant major climatic regions using LBNL WINDOW and TRNSYS (v.18) As a novel approach, the simulation study makes it possible to evaluate the overall performance of two proposed operational methods (insolation-based & storage-based) for adjusting water layer settings depending on climate conditions and reusability of the captured heat in water layer. Moreover, seven different base cases are presented in this study to compare the performance of proposed system, including conventional solutions (e.g. double or triple glazing with/without shading) and a state-of-the-art switchable technology (e.g. electrochromic glazing or EC). The results verify the hypothesis that the impact of SWFG compared to WFG varies depending on the climate with 0%-5.28% for hot climates and 0.25–3.39% for climates with heating demand. The discussion includes comparison with standard glass facades, where SWFG shows 47.41%-78.01% energy savings depending on climate. The climate-based approach of SWFG and WFG offers a significant shift in façade design that sees glass buildings as an opportunity for sustainability rather than liability for climate change. [ABSTRACT FROM AUTHOR]

This paper details a manual calibration of a 132 zones, highly monitored 5 434 m2 office building located in France. The calibration is conducted over one year of simulation at a five minute time-step. The model is mostly fed with input files containing measured data for each simulation time-step. First simulation results show that the model cannot be considered as being calibrated considering usual guidelines based on statistical indicators calculated on monthly energy consumption. Instead of going through a classical tuning process to match measured and simulated monthly energy consumption data, the choice was made to focus on explaining those discrepancies using available building information and monitoring data. A first dynamical heating and cooling powers analysis has been made, resulting in good correlation with measured values and explaining most of the discrepancies previously noticed, followed by a dynamical temperature analysis performed at the zone scale, and completed by a global statistical analysis at the building scale for the 132 zones of the building. Results showed very good agreement between measured and simulated temperature values with 90% of data points ranging between −1.72 °C and 2.02 °C of error. This demonstrate that a model that at first glance does not perform well considering particular statistical indicators can show an accurate dynamical behavior and a good performance when other variables and performance criteria are considered. This is followed by a discussion of the possible model improvements along with the limitations of statistical analysis compared to dynamic physical analysis for calibration performance assessment. The paper concludes with several proposals to improve the guidelines for calibration procedures involving the use of detailed monitoring data. [ABSTRACT FROM AUTHOR]

• An integrated energy-economic analysis of Net ZEB office buildings was proposed. • A thorough discussion on the energy balance was provided for Italian climate zones. • Energy grid integrations, self-consumption, storage and losses were analyzed. • The cost assessment of office buildings was performed and compared to state-of-art. • The energy-economic benefit of a new Net ZEB office in Italy was demonstrated. Recently, the built environment has become one of the leading sectors in the challenge of decarbonization and energy consumption reduction. Because of the specific use and the complexity of the typological, architectural, and technical characteristics, non-residential buildings represent a challenge to overcome the nearly Zero Energy Building standard and to achieve the objective of Zero or Net Zero Energy Buildings. It is desirable that the energy produced in situ from renewable sources totally covers the energy demand (ZEB); alternatively, the amount of energy self-produced and dispatched into the grid must be greater than (or equal to) the energy withdrawn from the grid (Net ZEB). At the same time, it is necessary not to neglect the economic aspects. The aim of this work is to propose an integrated energy-economic analysis to demonstrate the actual feasibility of new Net ZEB offices in different Italian climate zones. Thus, the energy analysis of a reference building, based on consolidated and cost-effective envelope and HVAC systems, has been carried out through dynamic simulations. The photovoltaic field located on the roof provides the energy needed for lighting, appliances, and HVAC. The obtained results are the energy demand and the energy production from renewable sources for the case study in each Italian climate zone. Moreover, the paper discusses the complexity of the annual energy balance, considering contributions such as grid integrations, self-consumption, energy stored and losses. Finally, the cost assessment of the proposed building has been carried out and compared to the state-of-art, pointing out the economic feasibility of the Net ZEB. [ABSTRACT FROM AUTHOR]

Strict energy performance requirements have reduced the building's operational energy use and related environmental impact in the past decades. Consequently, the embodied impacts of buildings gained importance, and hence focussing on the life cycle impacts has become essential to further reduce the environmental impact of buildings. Life cycle assessment (LCA) is an internationally accepted method used for this purpose. Current LCA studies of buildings typically look at their whole life cycle but ignore potential changes in operational energy use due to climate change. However, the latter is expected to influence the operational energy use of buildings to a significant extent. This paper aims to improve the assessment of the operational phase by including variations in the yearly operational energy use caused by climate change and expected changes in the electricity mix as a strategy for climate change mitigation. This adapted dynamic assessment method is used to assess a Belgian office building. The results indicate that a Representative Concentration Pathway (RCP) 8.5 climate change scenario causes a decrease of 18 % for the end use of gas for heating and an increase of 32.4 % for the electricity use for cooling and ventilation. For the case study, the reduction of the environmental impact due to the decrease in gas use compensates the impact increase due to the higher electricity use. An increase in environmental impact in future would however occur if both heating and cooling are provided by electricity. Furthermore, depending on the changes that will occur in the electricity mix, the global warming potential of the office building will change by −13 % for an electricity mix dominated by renewables and by + 10 % for one with mainly production by gas compared to the situation with the current mix. However, the inclusion of a more extended list of environmental indicators shows that the overall environmental impact will only reduce by 4 % or even increase by 7 % compared to the situation with the current mix. [ABSTRACT FROM AUTHOR]

Because the building sector has become a huge energy consumer, providing useful approaches can help tackle this important issue. Windows are an important component of a building since most of the natural light inside the building is provided by passing this component. Improper design of windows and their associated components, such as shadings, overheats the space and increases energy consumption, and on the other hand, disturbs the visual and thermal comfort of the building's occupants. The present paper aims to present a new approach for multi-objective optimization of the architectural specifications and control parameters of a smart shadow curtain. The use of this proposed approach leads to a significant reduction in building energy consumption and improved thermal and visual comfort. Energy-related simulations are performed through EnergyPlus software. Afterward, 21 design variables including control strategies and setpoints, location and orientation of shading, slat dimensions and material specifications, as well as hourly slat angle are considered in JEPLUS software. These simulations are conducted in four geographical directions over seasonal and annual periods for the city of Tehran, Iran. JEPLUS + EA software is employed to perform sensitivity analysis on the outputs of JEPLUS software and data optimization. Morris sensitivity analysis is implemented to investigate the effects of input variables on the objective functions. The NSGA-II algorithm is then applied to optimize the data to extract the optimal points on a Pareto front. The optimization results indicate that the external blind performs better than the internal blind and also the smaller the angle of the blind slat is, the better the visual comfort is but increases the electricity consumption for lighting. A comparison of the results of multi-objective optimization of controlled blind characteristics with initial values demonstrates that based on geographical directions, the total energy consumption of the entire building decreases by a range of 40–50 % per year, while visual comfort and thermal comfort are improved by ranges of 70–100 % and 10–40 %, respectively. [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]

The large public building energy consumption (BEC) is the focus of Building Energy Saving, therefore, it is necessary to study the characteristics of BEC to find out the important factors affecting BEC. For this purpose, 27 office buildings in the west of Inner Mongolia Autonomous Region were studied, based on statistical analysis of the researched basic information and energy consumption bill of these buildings. This paper focused on the determination of the significant factors affecting the total and subentry energy consumption intensity (ECI) of office building, as well as the establishment of standardized linear regression models between these selected factors and total and subentry ECI. Firstly, eleven continuous variables, three independent categorical variables, and the climate factor were selected and analyzed the impact on total and subentry ECI by statistical software SPSS20.0, in order to find out the significant influencing factors. Then based on the results of curve fitting, standardized models of total and subentry ECI and their respective significant impact factors were established using multiple linear regression analysis. The regression results showed that the electricity use percentage of the total equivalent electricity consumption was an important factor affecting the total ECI of office building in the west of Inner Mongolia Autonomous Region. Finally, univariate analysis of variance (ANOVA) was conducted between the three independent categorical variables and the total and subentry ECI, and the results showed that these factors had no significant effect on the ECI of office building. Process of the regressive model establishment and the results of analysis of variance could both guide us to propose more targeted energy saving measures. [ABSTRACT FROM AUTHOR]

For a long period, the energy use for domestic hot water (DHW) in non-residential buildings has been neglected. As energy use for space heating, ventilation, and lightning has been reduced through the years, the energy share for DHW production has increased. With today's focus on energy efficient buildings, the DHW systems must not be left behind the other fields to obtain a holistic approach to energy efficient buildings. To develop more energy efficient DHW systems for non-residential buildings, more knowledge is needed regarding draw-off durations, flow rates, and water temperatures from different draw-off types. This paper presents high frequency measurement results in two Danish office and educational buildings, which show that three DHW systems only utilize between 3.4 and 7.5 % of the total energy used for DHW production. Above 85 % of the total energy use is lost from the recirculation circuit to secure a high hot water temperature at the draw-off point. However, temperature measurements at the draw-off points show that a significant share of the heat is lost from pipes not covered by recirculation. One reason is draw-off durations are approximately 50 % of the time below 10 s at washbasins and kitchen sinks. Another reason is actual maximum flow rates are far from the calculated design flow. The high frequency measurements at multiple draw-off points give essential knowledge of use patterns for washbasins, kitchen sinks, and service sinks in non-residential buildings. This knowledge can contribute to developing energy efficient DHW systems and new types of DHW production. [ABSTRACT FROM AUTHOR]

The thermal properties of building envelopes have a major effect on energy consumption and indoor comfort, which is even more significant for glass buildings. The current solutions for mitigating this is either improving U-value and solar heat gain coefficient (SHGC) with additional layers, utilizing shading device or changing the transparency of the glazing system with adjustable properties. These solutions address the problem of energy consumption to a certain extent but cannot effectively manage and take advantage of the solar radiation effectively. Moreover, shading solutions obstruct the view to the outside and have negative impact on daylighting. This paper introduces Water-filled glass (WFG) technology, a transparent glazing system that utilizes water infill to improve thermal performance of the building envelope. The impact of WFG on cooling and heating energy demand is evaluated by studying different glazing configurations for WFG in different climates. Besides, the overall performance of WFG is compared with conventional double glazing with Low-E as base-case (with and without shading). The significance of this evaluation is that a comprehensive climate-based assessment considering absorption and insulation-based options for WFG is presented here for the first time: the paper evaluates different options of glazing system in combination with active systems in building via advance dynamic simulation over a typical year for all major Köppen-Geiger climates (13 cities). Additionally, as a novel approach, the simulation results presented here identify climates where improving SHGC is more ideal for thermal performance than improving U-value even if it occurs by compromising the latter and distinguishes absorption-based and insulation-based scenarios respectively. The results show that absorption-based energy model is a viable option, which results in energy savings between 3 and 84% in value of US$0.99-24.25/m².a depending on climate. The discussion includes simulation and comparison with identical spaces using standard technology that follows the local building code and conventions. The climate-based evaluation of water-filled glass can improve energy consumption of buildings and offer a sustainable strategy for glass envelopes by absorbing and reusing heat where transparency becomes an asset instead of being a liability. [ABSTRACT FROM AUTHOR]

• The energy performance of chilled water systems in a high-rise office building was examined. • Key indices were put forward to analyze the influence factors and their characteristics. • Systematic optimization methods were put forward from the aspects of external factors, internal factors and synergies between them. • Considerable energy saving effects were achieved on the premise of ensuring the indoor cooling demand. The chilled water system is the major component of HVAC systems, and it consumes a large amount of energy and has significant influence on the cooling effect. To save energy and provide a comfortable indoor environment, the energy performance of the chilled water system should be analyzed and improved. This paper introduces a systematic optimization process of the chilled water system in a high-rise office building. First, the influencing factors and their characteristics are summarized and analyzed, and the results show that higher temperature difference, energy efficiency of typical devices, and lower terminal resistance contribute to the improvement in energy performance. Then, based on the analysis, a systematic optimization method is put forward considering the external factors, internal factors, and synergies between them. Finally, an optimization of the studied building is conducted, from terminals to the chiller plant, and considerable energy saving effects are achieved on the premise of ensuring the indoor cooling demand. [ABSTRACT FROM AUTHOR]

• HVAC system stock is heterogeneous and changes over time. • A HVAC stock modeling and an archetype building energy modeling were integrated. • Disregard of the heterogeneity and its change can cause error in energy modeling. • Energy reduction by technological innovation relies on the share of the HVAC stock. Urban building energy model (UBEM) is an important tool in coping with energy issues of building sector. This paper focuses on an issue that the stock of heating, ventilation, and air-conditioning (HVAC) systems has been oversimplified in UBEM without considering its heterogeneity. It has been observed that this oversimplification may incur a significant error in the result. To address this issue, this paper proposes a framework that integrates a logistic regression based HVAC system stock modeling method and the bottom-up type engineering modeling method using building archetypes considering HVAC system configurations. A case study with Japanese office building stock revealed that (1) the disregard of the heterogeneity resulted in an error of 15% in the annual primary energy consumption, (2) the historical change in the HVAC system stock could decrease the consumption up to 5%, and (3) its composition has a significant influence on the energy saving due to energy efficiency increase in heat source machines used in HVAC systems. The presented method would contribute not only to improving the reliability of UBEM but also to providing an analytical capability of considering energy efficiency technologies related to HVAC systems along with the alternation of technology selection in the building stock. [ABSTRACT FROM AUTHOR]

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

• A novel three-stage decision-making approach for office buildings design considering 'Energy','Comfort', and 'Cost'. • The establishment of an optimization and decision-making model by applying NSGA-II and TOPSIS methods. • Two-year monitored data and post-occupancy evaluation for energy audit and thermal comfort. • The evidence of a case study demonstrates the feasibility of the proposed method and solutions. China, with the largest energy consumption system in the world, faces numerous challenges in achieving the government's commitment to reach a carbon-peak and carbon–neutral target. As the most common public building type in terms of floor area, office buildings have great potential for energy saving and emissions reduction. To meet this target, building designers target passive solutions that can meet the thermal comfort needs of occupants and also reduce energy consumption. This study aims to develop a decision-making method to select optimal solutions from among tens of thousands of design options considering the factors of energy consumption, comfort, and cost. We developed a novel optimization decision approach with the above-mentioned three objectives. The model consists of three stages: 1) the establishment of the reference building model, 2) sensitivity analysis to identify the main influencing variables and 3) the establishment of the optimization and decision-making model by applying NSGA-II and TOPSIS methods. By applying this three-stage decision-making model, this paper first proposes cost-effective passive design solutions for office buildings throughout the Hot Summer and Cold Winter climate zone. Finally, an office building in Shanghai was chosen as a case study to demonstrate the practical implementation of the proposed solutions through a post-occupancy evaluation with a two-year energy auditing and thermal comfort survey. It is evident that the proposed solutions provide support for the new low energy building design guide for office buildings along with necessary revisions to the existing standards for the Hot Summer and Cold Winter climate zone in China. [ABSTRACT FROM AUTHOR]

• A kNN-BS method is proposed to repair lighting and plug energy data. • Using cross-validation method to select the optimal k. • Using 2-step data classification method to decide day type. • Achieved higher accuracy than traditional k NN. The public building energy consumption monitoring platforms (BECMPs) play an important role in building energy conservation and energy-related decision-making. However, problems such as data missing and data mutation are quite common that they impede the effectiveness of the BECMPs. Based on the k NN algorithm, this paper proposes a k NN-BS data repair algorithm with the characteristics of electricity consumption trends taken into consideration. After data classification and cleaning, the data of hourly lighting and plug power consumption of an office building of Dalian University of Technology is repaired using the k NN-BS algorithm. The cross-validation method is used to determine the optimal k value. The relative repair error of the total daily electricity consumption of k NN-BS is less than 4% for working days and less than 6% for non-working days. Compared with k NN, the average CVRMSE of the k NN-BS algorithm is reduced by 1.36%∼1.59% for working days and 5.49%∼8.17% for non-working days. The k NN-BS algorithm shows excellent stability and robustness compared with k NN, polynomial and BPNN, which makes it very suitable for practical use, especially in efficiency-demanding scenarios. The k NN-BS algorithm can effectively improve the quality of public building energy consumption monitoring data and provide a theoretical basis and technical means for solving data quality problems in the BECMPs. [ABSTRACT FROM AUTHOR]

Double skin glazed facades (DGF) are an actual worldwide trend in new and refurbished buildings, even in warm climates with high solar radiation levels as in the Middle East and in Mediterranean climates. In such climates, overheating of indoor spaces and therefore excessive energy consumption are the main problems to be faced. These issues are commonly addressed mostly by thermal simulation or experimental evaluations on small prototypes. However, measurements on real-scale buildings with DGFs are very unusual. This information is crucial to understand and validate the predictions of the thermal behavior of the DGF technology. In this paper, thermal measurements were carried out in an unoccupied office building with a West DGF placed in the Northwest region of Argentina during 3 months in spring/summer. The air temperature exceeded in 10 °C the outdoor air and that indoor temperatures have not exceeded the outdoor levels in the hottest hours. Two correct design decisions were the use of low-e double hermetic glazing and the addition of a screenpainting to the external DGF panel. The experimental data showed that well-designed DGFs can reduce the summer energy consumption of buildings, even using West DGFs, in sunny climates. [ABSTRACT FROM AUTHOR]

In both the US and French building sectors, significant efforts have been undertaken to reduce the environmental impacts of new constructions notably their fossil energy consumption and related carbon emissions. This paper summarizes the results of a comparative evaluation of optimal design specifications for office buildings for selected locations in both US and France using life cycle cost analysis approach. First, an overview of both the US and French approaches for developing building energy efficiency standards and design of high performance buildings is outlined. Then, a general approach for design optimization of energy efficient office buildings is presented. Finally, selected results of the optimal analysis are discussed. In particular, it is found that optimizing life cycle costs (LCC) resulted on average in 30% in primary energy savings for office buildings located in the US and 40% savings in France. However, it is found that LCC-based optimal building designs do not meet the French energy efficiency RT 2012 standard. When accounting for typical operating conditions, photovoltaic (PV) panels are needed in order to comply with the RT 2012 required thresholds for the total primary energy consumption of the office buildings located in the French cities considered in the analysis. [ABSTRACT FROM AUTHOR]

Commercial office buildings represent the largest in floor area in most developed countries and utilize substantial amount of energy in the provision of building services to satisfy occupants’ comfort needs. This makes office buildings a target for occupant-driven demand control measures, which have been demonstrated as having huge potential to improve energy efficiency. The application of occupant-driven demand control measures in buildings, most especially in the control of thermal, visual and indoor air quality providing systems, which account for over 30% of the energy consumed in a typical office building is however hampered due to the lack of comprehensive fine-grained occupancy information. Given that comprehensive fine-grained occupancy information improves the performance of demand-driven measures, this paper presents a review of common existing systems utilized in buildings for occupancy detection. Furthermore, experimental results from the performance evaluation of chair sensors in an office building for providing fine-grained occupancy information for demand-driven control applications are presented. [ABSTRACT FROM AUTHOR]

China building energy codes (CBEC) have been introduced for over two decades but little has been publicized in literature. LEED on the contrary is the most publicized building environmental assessment scheme. To enable better understanding of the compliance standards of CBEC, this paper presents the energy performance assessment results (represented by energy and energy cost savings) of three office buildings in China (one in Beijing and two in Shanghai) using the current versions of CBEC and LEED. The energy and energy cost savings of the three buildings were predicted based on hour-by-hour simulations using the weather data and energy tariffs of Beijing and Shanghai where the three studied buildings are located, and their actual building and system characteristics. The study revealed that LEED in general sets more stringent requirements than CBEC in indoor design conditions, building envelope characteristics and air-conditioning system features. Amongst various building end uses, energy use for air-conditioning was found dominating the assessment results, and the use of energy efficient measures not forming part of the baseline criteria, could lead to 2 to 5% reduction in the overall building energy use. The two schemes were benchmarked against BEAM Plus and their weaknesses were also unveiled. [ABSTRACT FROM AUTHOR]

• Control design for coupled natural ventilation and thermally active building system. • Assessment of operational performance using one-year high-fidelity measured data. • The annual heating and cooling energy consumption is 12.8 kWh/m2. • Issues identified and lessons learned for further improving the design and operation. The thermally active building system (TABS) has emerged as an energy-efficient measure for cooling and heating buildings since it facilitates the use of low-energy technologies, such as free cooling with geothermal sources and heating with low-temperature heat pumps. However, the control of TABS is challenging due to its high thermal inertia. Though a considerable amount of research has been done using simulations or small-scale experiments, few studies have published detailed long-term measured data of TABS operation. In addition, there has been little discussion on the control integration of slow-acting TABS with other fast-acting systems, such as natural ventilation. To fill these gaps, this paper presents a comprehensive assessment of the operational performance of the coupled NV and TABS using one-year high-fidelity measured data of an experimental small office building with an extensive sensor network located in Cambridge, Massachusetts. First, the coupled system and control design are described. Then, the thermal, energy and detailed control performance are assessed by comparing with design intents and simulated predictions. It is found that the coupled system demonstrates its effectiveness to control indoor thermal environment while achieving high energy efficiency. The annual energy consumptions are 11.5 kWh/m2 for heating and 1.3 kWh/m2 for cooling. The heating COP, including heat pump and two water pumps, is 3.5, and the cooling COP is 18.3 by utilizing free cooling from geothermal resources. The issues in operation are identified, including system/equipment faults, implementation of controls, and settings by building operators. Finally, lessons learned from this project are discussed and the strategies for improvement are summarized. [ABSTRACT FROM AUTHOR]

A large part of energy consumption in homes, offices and commercial spaces is related to Heating, Ventilation and Air-conditioning (HVAC) devices. The effective parameter on the consumption of HVAC systems is internal heat gains that arise from occupants, electric equipment and lighting. In order to reduce the energy consumption of these systems, internal heat gains should be predicted accurately. Since there are few investigations performed on the prediction of internal heat gains, in this paper, three predictive models, namely multiple regression model, Levenberg–Marquardt back-propagation (LM-BP) model and similar days method based on combined weights, have been deployed. By assessing the influential factors on internal heat gains, fundamental theories, structures, equations and parameters of these models are thoroughly proposed. To examine the prediction techniques, an office building in China was considered. It was found that all the proposed models have high accuracy; however, the LM-BP neural network showed the most precision among other models with RMSE = 15.59, MAE = 10.16 and MAPE = 6.35. This model had a higher agreement with the actual internal heat gains compared to the predetermined working programs in the ASHRAE standard 90.1. The proposed models used in this study can lead to providing a theoretical base for scholars and engineers to improve the predictive control of HVAC systems, which plays an important role in enhancing thermal comfort, saving energy of residential buildings. [ABSTRACT FROM AUTHOR]

• A method is to disaggregate bulk meter data of commercial buildings into end uses. • The method uses building automation system data to disaggregate bulk energy use. • The method is demonstrated with data from a submetered academic office building. • The method accurately disaggregated the heating and electricity end uses. Although understanding end uses in large commercial and institutional buildings is of great utility for achieving energy efficiency, most new and existing buildings still lack adequate submetering except for large end uses. End use disaggregation methods offer an alternative to fulfill weaknesses in a metering network, especially in large commercial and institutional buildings. Unlike non-intrusive load monitoring (NILM) methods that mostly rely on high frequency power consumption data and focus on residential buildings, this paper introduces an end-use disaggregation approach using building automation system (BAS) data. The BAS data provide information about the operational status of main energy-consuming systems and equipment such as fans, pumps, air handling unit (AHU) heating coils, and variable air volume (VAV) terminal reheat coils. The proposed method uses a suite of multiple linear regression models estimated by the genetic algorithm and a least-square solver to disaggregate heating and electricity use data using BAS data as predictors. The method was demonstrated with data from an academic office building in Ottawa, Canada. The accuracy was evaluated by comparing the disaggregation results to ground truth energy use data acquired from dedicated submeters. The findings suggest that the method can accurately disaggregate building-level hourly electricity and heating energy use into following end use categories, for electricity: (a) occupant-controlled loads (lighting and plug loads), (b) distribution loads (the electricity used by the AHUs); and for heating: (a) energy used by the AHU heating coils, (b) perimeter heating devices (reheat coils/and radiators). [ABSTRACT FROM AUTHOR]

• A dual-objective optimization method from the perspective of designer and user. • Life-cycle energy and economy optimization for building Design Period based on HypE. • Use Low-E insulating glass, rotate the building 18°counterclockwise are beneficial. • Life cycle energy consumption and economy of the case are reduced by more than 15%. Multi-objective optimization of building life cycle energy consumption and cost is very essential for guiding design quantitatively. However, previous studies paid more attention to the potential effect of building energy saving and cost reduction based on design parameters from the perspective of designers, but ignored the potential brought by the willingness of users during operation stage. Therefore, this paper proposes a dual-objective optimization method based on the HypE algorithm, which takes into account users' energy saving intentions in order to maximize the potential of building life cycle energy saving and cost reduction. The application premise of this method is to establish energy and cost calculation model of building life cycle. The method was applied to the design of a small office building in Xi'an city of China, and the scheme were optimized by adjusting 7 design parameters and 2 parameters (cooling and heating temperature) related to the energy saving willingness of the users. The results show that the proposed method can reduce the energy consumption by 15.1%-18.8% and the cost by 2.2%-10.1% during life cycle. The results will continue to be reduced by up to 6.5% and 12.02% if the potential of uers' willingness to save energy continues to be realized. It is proved that the proposed method contributes to the comprehensive evaluation of energy saving and economic effects of architectural design during its life cycle and provides effective theoretical guidance and practical tools for architectural design and optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • Building energy consumption is largely driven by diversity in occupant behavior. • Machine learning can be used to predict lighting energy use with occupancy data. • Building layouts are optimized with a naïve clustering method and genetic algorithm. • Layout optimization can yield 5–6% reduction in lighting energy use. One of the primary driving factors in building energy performance is occupant behavioral dynamics. As a result, the layout of building occupant workstations is likely to influence energy consumption. In this paper, we introduce methods for relating lighting zone energy to zone-level occupant dynamics, simulating energy consumption of a lighting system based on this relationship, and optimizing the layouts of buildings. The optimization makes use of both a clustering-based approach and a genetic algorithm, and it aims to reduce energy consumption. We find in a case study that nonhomogeneous behavior (i.e., high diversity) among occupant schedules positively correlates with the energy consumption of a highly controllable lighting system. We additionally find through data-driven simulation that the naïve clustering-based optimization and the genetic algorithm (which makes use of the energy simulation engine) produce layouts that reduce energy consumption by roughly 5% compared to the existing layout of a real office space comprised of 151 occupants. Overall, this study demonstrates the merits of utilizing low-cost dynamic design of existing building layouts as a means to reduce energy usage. Our work provides an additional path to reach our sustainable energy goals in the built environment through new non-capital-intensive interventions. [ABSTRACT FROM AUTHOR]

• Reviewing limitations of previous data-mining based FDD methods on HVAC systems. • Knowledge-based and model-based systems have limitations in complicated FDD process. • In FDD in large-scale HVAC systems, driven methods are superior to other methods. • Hybrid methods have great potential in finding naïve faults in FDD process. • Developed hybrid methods can overcome delays, and get less false and missed alarms. Abnormal operation of HVAC systems can result in an increase in energy usage as well as poor indoor air quality, thermal discomfort, and low productivity. Building automated systems (BAS) collects a massive amount of data related to the operation of each component of HVAC systems. Although BAS has been implemented in many buildings over the past decade, the collected data have not been analyzed thoroughly. Some studies have relied on data-mining methods to predict, detect, and diagnose faults in HVAC systems. This paper critically reviews the existing literature and identifies the research gaps in data-driven data mining fault detection and diagnosis (FDD) methods studies on HVAC systems. In this review, data-driven based FDD methods are classified into three classes, namely supervised, unsupervised, and hybrid-learning methods. The hybrid approaches are introduced as the preferred methods among the existing approaches to be used in online FDD processes. Furthermore, some components of HVAC systems and their potential faults are discussed in detail. The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones. Moreover, an optimal approach could involve both supervised and unsupervised learning (hybrid methods). [ABSTRACT FROM AUTHOR]

• A local sensitivity analysis of double skin facades' (DSFs) features is presented. • Simulations are carried out with two building energy simulation tools and compared. • In-built models for DSF (climate façade) are compared with experimental data. • The ranking and magnitude of sensitivity indices are similar for the two tools. • Glazing thermal and optical transmittance are the most relevant variable in DSF design. Double skin façades (DSFs) are considered façade technologies that can reduce energy use and improve occupant comfort due to their advanced features. Their design requires reliable simulations due to their complex thermophysical behaviour, which are often carried out by practitioners using building energy software (BES) tools. Using an exhaust-air façade (also called climate façade) case study, the paper analyses the sensitivity of in-built DSF models in two popular BES tools (EnergyPlus and IDA ICE) for different orientations and climates. Small variations in input variables were considered to identify the parameters that the designer should pay most attention to during the design of the DSF according to different performance indicators. The results show that, regardless of the climate or orientation, the optical properties of the system (glazing and shading) were the most important in determining its performance, followed by the thermal properties of the glazing, while the geometrical, airflow and frame characteristics were less relevant. The model validation process also showed how differences in the in-built models (i.e. the use of a capacitance node for the glazed layers) lead to a difference in the reliability of the two BES tools. [ABSTRACT FROM AUTHOR]

• Wall-type heat recovery units are solution for improving ventilation in refurbishment. • Assessment of air stream mixing by visualization and CO 2 measurements was conducted. • The risk of air mixing when using wall-type heat recovery units is low. • The analysis of the annual energy demand for residential buildings was conducted. • Wall-type heat recovery units are helpful in achieving low energy buildings. In the cold and moderate climates the energy demand for heating of fresh ventilation air is crucial. The mechanical ventilation systems with heat recovery are used to decrease the peak power of the heating system and to save the energy. In a case of wall-type heat recovery units there is a doubt about the possibility of mixing fresh and exhausted air in the combined intake-outtake device. In this paper experimental investigations were conducted to assess their hygienic safety. The original set-up containing test chamber and the equipment for CO 2 concentration measuring has been used. Visualization of airflows mixing with the usage of smoke is presented. The results show that the risk of the exhausted air return to the building is low. At the same time the ventilating efficiency of such systems is high. The analysis of the annual energy demand for the sample single-family and multi-family buildings has been conducted to present the possible energy benefits of usage such devices. Concluding, the wall-type heat recovery units should be considered as hygienic safe and helpful devices in achieving the standard of low energy buildings in a case of existing and refurbished ones. [ABSTRACT FROM AUTHOR]

• A retrofit selection strategy employing economic and environmental decision criteria was proposed. • Building energy retrofits were evaluated through a life cycle thinking approach. • Retrofits with operational emissions savings may not be beneficial when considering life cycle impacts. • Energy mix and the heating system of the house greatly impact the effectiveness. • Retrofit selection changes with stakeholder priorities on environment and economics. Improving the energy performance of buildings has been a much-discussed topic over the past few decades. With the current focus on climate change mitigation, emissions reduction has also come to the forefront of this discussion. Retrofitting is an option to improve the energy and emissions performance of buildings. However, in the residential building stock, retrofit planning for existing buildings faces many complexities due to variations in climatic conditions and macro-environment as well as the presence of multiple stakeholder groups. Thus, in identifying solutions, it is necessary to take a holistic perspective that covers these different dimensions. In this paper, commonly used building energy retrofits were evaluated through a life cycle thinking approach. The performance of various retrofit options applicable for single family detached housing were evaluated using the HOT2000 energy simulation software package under the varying climatic conditions and energy supply scenarios across Canada. The retrofits were evaluated in terms of the additional investment, energy use and cost reduction achieved over the life cycle, and life cycle emissions reduction. The findings indicate that the provincial energy mix and the heating system of the house (i.e. electric or natural gas) play a major role in determining the effectiveness of a retrofit, and this "effectiveness" changes at different stakeholder levels. However, not all retrofits that reduce emissions make economic sense and vice versa when life cycle thinking comes into play. The findings will be useful for building owners and occupants as well as for policy developers and other decision makers interested in demand side management. [ABSTRACT FROM AUTHOR]

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

• Bio-inspiration is a promising approach for energy efficiency. • Bio-inspired cooling technologies focusing on buildings were reviewed. • Review according to the classification of building elements and application scale. • Heat transfer mechanisms behind each technology were also discussed. In response to the growing demand for indoor environmental quality (IEQ) and energy efficiency, abundant innovative bio-inspired cooling technologies have been proposed and their applications in buildings have been greatly demonstrated in the previous decades to enhance the benefits of building occupants. IEQ is associated with human health and productivity but maintaining good IEQ requires continuous air-conditioning resulting in a high energy consumption, especially space cooling. Bio-inspired cooling technologies focus on the fundamental mechanisms of heat transfer used by animals or plants which are considered as the keys to create a harmony between buildings and the nature, whereby IEQ can be enhanced while achieving energy efficiency. This review provides a comprehensive summary on the current bio-inspired cooling technologies, including the concepts in the research stage and the well-developed products applied in buildings, and discusses some promising designs that have the most potential for future applications. This paper is structured according to building elements, in which technologies regarding HVAC system, building materials, opaque building envelope and transparent building envelope are reviewed. The heat transfer mechanisms behind each technology including conduction, convection, evaporation or phase change and radiation are discussed. Yet successful green buildings involve a smart thermal management system for which a section is dedicated to discussing various approaches in design optimization. In the last section, a case study simulation of implementation of bio-inspired cooling technologies in a house and its energy efficient performance are analyzed. The authors attempt to motivate the future research and development in energy efficient buildings. [ABSTRACT FROM AUTHOR]

• Design a reasonable electricity pricing programs for the high renewable penetration scenario. • Reveal the importance of electricity pricing programs on EEMs selection in U.S. medium office buildings. • Help building owners to select optimal EEMs under different electricity pricing programs. Building owners usually select energy efficiency measures (EEMs) by referring to return on investment (ROI). Current studies tend to apply static energy price to estimate ROI. However, more and more buildings are adopting dynamic electricity pricing programs. To understand how electricity pricing programs impact the selection of EEMs, this paper presents an analysis of the ROIs of EEMs under different pricing programs using U.S. medium office buildings as an example. Eight EEMs in four typical cities are selected as case studies. Considering five electricity pricing programs scenarios (one static program and four dynamic programs), EEMs are selected based on their ROIs. The main findings are: (1) The ROIs of EEMs change under different pricing programs. (2) In Honolulu, Buffalo, and Denver, replacing interior fixtures with higher-efficiency fixtures has a significantly higher ROI than the rest EEMs under all five pricing programs. However, the ROI of this EEM in Honolulu ranges from 28% to 47% for different pricing programs. (3) Similarly, in Fairbanks, replace heating coil with higher-efficiency coil produce higher ROI than the rest under all five pricing programs. (4) For other EEMs, their ROI rankings vary according to electricity pricing programs. [ABSTRACT FROM AUTHOR]

• Urban form factors that affect heating load in severe cold regions are discovered. • Heating load can be saved by 6.76% with every unit increased in surrounding FAR. • Heating load can be saved by 12.76% with every unit increased in surrounding RHR. • How urban morphology influence heating load in severe cold regions is discussed. • Urban design strategies are provided for reducing heating load in severe cold regions. Studies have suggested that the energy demand of buildings can be significantly influenced by urban morphological factors. However, little research has focused on urban-morphology-related heating energy consumption in severe cold regions, where buildings are facing greater pressure to save energy because of long heating periods. In this paper, the aim is to fill this knowledge gap through a combination of energy simulation and statistical analysis. First, taking 73 office buildings in Harbin as the study objects, the theoretical energy consumption (TEC) is stimulated and the external influence energy consumption (EIEC) of each building is obtained by subtracting the TEC from the measured actual energy consumption. Then, using regression analysis, the effects of 7 urban morphological factors on heating energy consumption are evaluated. The results indicate that greater building site cover (BSC), floor area ratio (FAR), building height (BH), road height-width ratio (RHR), total wall surface area (WSA), and lower green space ratio (GSR) are beneficial to the reduction of heating energy consumption. Among them, FAR is the most critical factors in saving heating energy by up to 10.820 kW·h/m2/y, with the addition of one more unit. This result confirms the significant role that energy saving-oriented urban morphology can play in making heating energy more efficient and sustainable in severe cold region cities. [ABSTRACT FROM AUTHOR]

• A gamification platform to foster energy efficient behaviors in office buildings. • The gamification platform is a mobile application with interactive dashboards. • The mobile gamification platform was demonstrated in an office building. • The demonstration results suggest electricity savings of 20%. Office buildings consume a significant amount of energy that can be reduced through behavioral change. Gamification offers the means to influence the energy consumption related to the activities of the office users. This paper presents a new mobile gamification platform to foster the adoption of energy efficient behaviors in office buildings. The gamification platform is a mobile application with multiple types of dashboards, such as (1) an information dashboard to increase the awareness of the users about their energy consumption and footprint, (2) a gaming dashboard to engage users in real-time energy efficiency competitions, (3) a leaderboard to promote peer competition and comparison, and (4) a message dashboard to send tailor-made messages about energy efficiency opportunities. The engagement and gamification strategies embedded in these dashboards exploit economic, environmental, and social motivations to stimulate office users to adopt energy efficient behaviors without compromising their comfort and autonomy levels. The gamification platform was demonstrated in an office building environment. The results suggest electricity savings of 20%. [ABSTRACT FROM AUTHOR]

• Investigation of two residential buildings in a sub-Arctic region. • One building subject of energy-retrofit, one maintained the original envelope. • No significant improvement to the heat supply after energy efficiency measure. • The thermal indoor climate showed signs of overheating. • The heating system should be reviewed when an energy-retrofit is done. There is a need to improve the understanding and the knowledge of energy efficiency measures for residential buildings in sub-Arctic climate regions. This paper presents an investigation of two identical multi-family residential buildings in the sub-Arctic climate of northern Sweden, before and after renovation. During the renovation, additional insulation of the external walls and new windows were installed in one building, while the other building retained its original envelope. The energy usage data for the past four heating seasons were collected, including data from before and after the renovation. Detailed thermal indoor climate data were gathered for specific months. The data from the two separate buildings showed that the renovation did not result in a significant improvement in energy usage. Prior to the renovation, the energy usage data showed a difference of 2–3% in the heat supply between the two buildings, and this difference persisted after the renovation. On the other hand, the indoor air temperature was raised. The renovated building had an indoor air temperature which was 2 °C higher than the not yet renovated building. IDA ICE models were constructed and validated with the measured data to investigate how a lower indoor air temperature would affect the energy usage and indoor thermal climate. The models showed that with a reduction in the indoor air temperature by 2 °C after the renovation, the thermal climate would maintain an acceptable level according to PMV/PPD standards, and would result in a 13–14% reduction of the heat supply during the cold months. With an annual reduction of 15%, the heat supply could be reduced by 270 MWh per year for the whole area where the buildings are located. This clearly demonstrates the importance of adjusting the heating system after an energy efficiency measure has been performed. [ABSTRACT FROM AUTHOR]

• Deep energy efficiency retrofits are financially feasible in Ireland with grant aid. • Without grant incentives, only shallow retrofits of homes are economically feasible. • Environmental impact of various retrofit designs for homes built 1991–2002 assessed. • Best solution differs depending on life cycle environmental indicators considered. • Environmental impact of materials from 20 to 100% of share of building's life cycle. 1.9 million housing units in Ireland are required to be retrofitted for the existing Irish national housing stock to be considered nearly zero energy building (nZEB). This paper assesses optimum retrofit packages aimed at improving the building material thermal efficiencies and energy demand of gas-heated semi-detached and end-terraced houses in Ireland. The cost-optimal methodology framework for calculating cost optimal levels of minimum energy performance requirements for buildings and building elements is used to determine the optimum retrofit packages from both a householder perspective and a societal perspective. The results of the cost-optimal approach are compared to the results from a weighted framework approach which incorporates life cycle environmental and cost indicators from both a householder perspective and a societal perspective. The results found that the ranking of energy efficiency retrofit designs based on multiple life cycle environmental and cost indicators differ compared to cost optimum designs based on only life cycle energy and cost indicators. The cost-optimal approach was found to be effective for identifying retrofit packages that are among the best packages even without accounting for multiple environmental indicators. However, once multiple indicators were considered, the hierarchy of the optimal retrofit packages changed. In the Irish context, the environmental impact of the Irish electricity grid was found to play a significant role in the hierarchy of the retrofit packages examined. While the cost-optimal method should not be relied on solely for identifying the optimum retrofit package solution, it could be employed as part of the multistage assessment methodology for narrowing down design solution sample sizes. Using the remaining array of retrofit packages solutions, the optimum retrofit package could be identified using multiple indicators. [ABSTRACT FROM AUTHOR]

• We selected three benchmarking methods to establish building energy benchmarking models based on a sample of 45 hotel buildings in China. • We compared the three benchmarking methods in terms of consistency, robustness and explanatory ability. • The energy efficiency performance obtained from the three methods were found to be inconsistent. • We came up with suggestions based on comparative analysis for policy-makers to develop building energy benchmarking programs. A reasonable building energy efficiency benchmarking program plays an important role in energy consumption control and supervision. Previous studies have focused on the process of establishing a single benchmarking method, but few have compared the performances and outcomes of different methods. To fill this gap, this paper selects three benchmarking methods—multiple linear regression (MLR) based on Energy Star, stochastic frontier analysis (SFA) and the descriptive statistics method (DSM) based on the national energy consumption standard in China—to develop benchmarking models. We demonstrate each method using data on the energy and building characteristics of 45 four- and five-star hotel buildings located in Chongqing, China. To compare the consistency, robustness and explanatory ability of the three methods, we first utilize the Spearman rank correlation analysis to test whether these methods have consistent energy efficiency ranks and then present Sankey diagrams to further reveal the interactions of the estimated energy efficiency grades obtained from the three methods. It is found that the results of DSM and SFA are most consistent, while MLR vs. SFA and MLR vs. DSM present significant differences in evaluating building energy performance. In addition, DSM is more robust for evaluating the ranks of sampled buildings, while SFA is more robust for evaluating energy efficiency grades. Furthermore, we discuss the explanatory ability of each method. In addition to the building characteristics, the design and operational characteristics of the HVAC system have great effects on building energy consumption. Finally, we present suggestions for policy-makers regarding the development and implementation of the building energy benchmarking program in Chongqing and for the management of buildings with different energy performances to further improve the energy efficiency. [ABSTRACT FROM AUTHOR]

• A replicable methodology for identifying reference buildings is presented. • The k -means clustering method is applied to the energy performance certificate database. • To illustrate the methodology, an EPC database including 13,701 offices is used. • Sixteen clusters and corresponding representative offices are identified. • Representative offices can be used for cost-optimal retrofitting analysis of the entire stock. A large number of buildings must be evaluated to formulate energy retrofitting policies for existing building stock. In this context, it is crucial to identify reference buildings that can effectively represent the entire stock, since such buildings can then be used to assess the individualized cost-effectiveness of retrofitting measures. This paper presents a novel approach for identifying and defining a set of reference buildings by applying the k -means clustering method to energy performance certificate databases. To this end, a four-step methodology has been envisaged. First, an energy performance certificate database is prepared and variables that have an impact on energy consumption are pre-selected. Selected data are then pre-processed. Next, the k -means clustering method is applied. Finally, the resulting cluster centroids are used to identify the closest energy performance certificates in the database, in other words, the representative buildings that will then be used for cost-optimal retrofitting analysis. The methodology is illustrated using the energy performance certificate database managed by the Catalan Institute of Energy (ICAEN), which includes a sample of 13,701 offices. Due to the large number of missing values in the database, the k -means clustering algorithm was finally performed over 6,083 energy performance certificates. Seven representative office blocks and offices in industrial buildings and nine representative offices in residential buildings were identified. The results establish the basis for supporting strategic decision-making for energy saving retrofit interventions in existing Spanish offices. [ABSTRACT FROM AUTHOR]

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

• A convection-radiation system was set up in an office building in Mianyang, China. • The system can reduce energy consumption under intermittent operation. • The energy consumption of the system under different control strategies is evaluated. Convection-radiation combined cooling systems are commonly used in residential buildings in hot summer and cold winter areas because of their high thermal comfort. When this system is used in office buildings, combined with the characteristics of office building's working time occupancy patterns and building typologies, the operation strategy can lead to different energy consumption under the same thermal comfort. In this study, three operation strategies are compared to a system that combines two different terminal units in the same thermal zone: a radiant floor and fan coil. The performance of the combined system is assessed in terms of the thermal comfort level, hourly refrigeration performance coefficient, and energy consumption. The results indicate that the system with an intermittent operation strategy reduces energy consumption by approximately 39% compared with a continuous operation strategy, with little impact on thermal comfort. The room temperature under the three control strategies meets the temperature requirements of the office building during working hours. The fluctuation in room temperature is smaller under the continuous operation strategy than under the intermittent operation strategy, which indicates that the thermal environment of the room has better stability during continuous operation. The maximum indoor vertical gradient is always less than 3 °C, which meets the ASHRAE55-2017 standard. The room-temperature fluctuations are strongly affected by solar radiation. Therefore, building shading measures should be considered in building designs to avoid local discomfort or energy waste. When the system operates stably, the Predicted Mean Vote (PMV) value is low. The reason for this is that the radiant cooling system reduces the average radiant temperature of the building to adjust the room temperature. Therefore, it is recommended that the indoor design temperature of the radiant cooling system be increased by 2–3 °C to ensure better indoor comfort. [ABSTRACT FROM AUTHOR]