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A significant focus of research and new technologies for reducing energy use in buildings is on operating the systems more efficiently during the times when the systems are active. However, there is a large potential for energy savings in determining the periods when the systems should or should not be active. This scheduling aspect of operation is often overlooked even though relatively simple solutions can unlock substantial energy savings. This paper describes a smart building start (SBS) algorithm that considers multiple zones in a building to determine individual schedules for room controllers as well as the central systems based on solving a simple optimization problem. Application of the SBS algorithm to multiple interconnected systems enables a staggered start-up that minimizes peak loads and also ensures comfort is within a target range with minimal system run time. The SBS algorithm extends the capability of traditional optimal start and is designed to be simple to deploy and robust. Simulation results as well as results from tests in a real building with a VAV system are presented. The presented algorithm is applicable to any type of building with a zonal or multi-zone HVAC system. To function, it needs to be able to change setpoints in rooms and monitor room temperatures, as well as, if desired, turn the central system on or off. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two modules were analyzed during Antarctic summer at Spanish research station. • Measured transmittances and infiltrations were excessively high, resulting in high energy consumption for heating. • Indoor air conditions were poor in both modules, particularly the CO 2 concentration. • Extreme weather conditions accelerated the degradation of insulating materials and sealants. • Energy efficiency standards are urgently needed to improve the carbon footprint of Antarctic bases. In this paper, we present the energy characterization of two habitable modules at the Spanish station "Gabriel de Castilla", located on Deception Island, Antarctica. The analysis includes profiles of energy consumption for space heating and domestic hot water, as well as indoor air conditions, which were all recorded during one month of Antarctic summer. Additionally, the measurement of walls' transmittance and air infiltrations, together with thermography analysis, enabled evaluating the conservation state of the buildings' envelope. Upon analyzing all this data, it is evident that there is potential to enhance the design of future modules, improving the thermal comfort while significantly reducing the energy consumption. This will contribute to decrease the logistics and environmental impacts of these research stations, crucial considerations in human activity on the Antarctic continent. [ABSTRACT FROM AUTHOR]

• Automatic MiC envelopes and layout generation by a novel SSG-MOO method. • Multi-objective optimization formulation for passive MiC design. • A pilot MiC study in Hong Kong produced 5 selected Pareto optima out of 625. • Up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement against the baseline. • A multi-level analysis of results and design strategies for practitioners. Modular-integrated Construction (MiC) is an emerging construction technique promoted in the building sector for high productivity and low waste emission in the construction phase; yet, the standardized modules also bring new challenges, such as balancing passive energy efficiency and spatial daylight autonomy, to the operational phase. This paper proposes a Symmetric Skeleton Grammar-based Multi-Objective Optimization (SSG-MOO) method to formulate parametric MiC envelopes and detailed layout, with the two objective functions being energy efficiency and interior daylight performance in the operational phase. Pareto optima of the SSG-MOO, computed by the Non-dominated Sorting Genetic Algorithm II, are generally verified and analyzed in three levels, i.e., MOO's solution space, SSG layout, and MiC design parameters. A case study of MiC residential building in Hong Kong demonstrated the SSG-MOO method through five new passive MiC designs (i.e., spatial reorganization of three architectural modules, and parameter tuning of the envelops and corridors), achieving up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement compared to the baseline design. The contribution of this paper is two-fold, including a novel and sound SSG-MOO formulation for parametric MiC designs, and offering time-efficient and evidence-based design tactics for MiC designers and industrial practitioners to push boundaries of MiC. [ABSTRACT FROM AUTHOR]

[Display omitted] • The LCA methods and findings of 41 papers on air-conditioning systems are examined. • Retrieved studies presented discrepancies in the LCA goals and scope definition. • The global warming potential (GWP) was the most evaluated indicator across papers. • Grid-connected electrical heat pumps generally had a higher life cycle GWP. • Improvements related to AC energy sources and other life cycle aspects are proposed. Air conditioning (AC) has become the fastest-growing energy end-use in buildings worldwide, and its adoption is expected to increase further due to various socioeconomic factors and climate change. Despite this, the decarbonization of space cooling is progressing slowly; hence, the emissions burden at each stage should be examined in detail. Given this context, it is crucial to assess the environmental performance of AC systems by considering a comprehensive approach like the Life Cycle Assessment (LCA). While several studies have already targeted the LCA of AC systems, a clear overview of those studies' methodologies, assumptions, and results is still lacking. Therefore, this systematic review examined the methods and key findings of 41 peer-reviewed articles. Although the results revealed discrepancies among the goals and scope, impact assessment methods, and indicators, most articles predominantly focused on the global warming potential (GWP) impact, and its reported quantification was further analyzed. It was observed that grid-connected vapor-compression heat pumps/chillers (conventional systems) presented higher GWP impacts than other AC systems powered by renewables, which in contrast, exhibited higher impacts in some toxicity and resource depletion-related indicators. However, the share of GWP showed different distributions, as the pre-operation emissions of solar-powered AC systems (either PV or thermal) were higher than those of conventional systems, mainly driven by reduced energy consumption usage. This review concludes with some recommendations for each stage of the life cycle. Since the operation phase generally showed the highest impact, more efforts should be devoted to accelerating the transition to a 100% clean electricity power system at a national scale. In contrast, the need to power AC systems with on-site renewables should be carefully assessed based on the context. [ABSTRACT FROM AUTHOR]

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. The paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools. • First MPC demonstration in K-12 school buildings for peak demand reduction and load shifting. • Experimentally demonstrated a scalable MPC for multiple RTUs without hardware retrofit. • Hierarchical MPC is improved in consideration of practical challenges such as user's override and poor data resolutions. • Achieved 24% peak demand reduction and showed 16% of load shifting potential. • Share lessons learned during the demonstration of practical challenges. [ABSTRACT FROM AUTHOR]

[Display omitted] • Identifying recent developments in thermal-driven dehumidification technologies. • Evaluating critical characteristics including dehumidification efficiency, energy consumption and cost. • Assessing the desiccant properties for thermal driven dehumidification technologies. • Directing future research to address environmental concerns via innovative sustainable humidity control mechanisms. As we navigate an era marked by significant environmental concerns and technological advancements, the search for energy-efficient and ecological friendly solutions is more critical than ever. One area of growing significance is the control of indoor humidity levels, which not only impacts human comfort and health but also the structural integrity and air quality within buildings. The challenge is to control these levels while lessening energy consumption and environmental impact. A feasible solution lies in the use of thermal-driven dehumidifiers, a promising technology that offers a sustainable approach to humidity control. Thermal-driven dehumidifiers have emerged as promising solutions for humidity control in various applications, owing to their energy-saving and eco-friendly features. Thus, the focus of this review paper is to explore the most recent advancements and enhancements in thermal-driven dehumidification technologies. This includes systems based on solid desiccant, liquid desiccant, and thermoelectricity. The study aims to fill the existing literature gaps, providing a thorough analysis of the current state of these technologies, their performance indicators, and their potential for future development. The study also explores the suitability of these technologies for different ambient conditions and humidity loads. It addresses critical characteristics such as dehumidification efficiency, cooling capacity, regeneration heat, and energy consumption. Each technology is thoroughly examined, including its suitability for various ambient conditions and humidity load scenarios. Specific real-world examples are examined to highlight these technologies' practical importance and possible influence in tackling humidity management concerns. [ABSTRACT FROM AUTHOR]

[Display omitted] • Frosting & defrosting characteristics of appliances are reviewed from 2000 to 2023. • Parameters affecting frost formation in 'no-frost' evaporators are surveyed. • Methods for improving frosting and defrosting performance are summarized. • Correlations of j and f factors for 'no-frost' evaporators are summarized. • Major findings and recommendations for future research are presented. Refrigerators and freezers are now common household devices, and they consume a considerable amount of energy each year. The deposition and accumulation of frost on the surface of the refrigerator evaporator is inevitable and always has a significant negative impact. The periodic defrosting is necessary for household refrigerators, which increases the energy consumption and the compartment temperature. This paper presents the key findings concerning the frosting and defrosting characteristics of refrigerators and freezers based on the existing literature in 2000 ∼ 2023. In the first part, the frosting research is summarized. Firstly, the parameters affecting the frost formation of the refrigerator evaporator are analyzed. Then, the structure of the 'no-frost' evaporator is introduced. Meanwhile, the heat transfer and pressure drop characteristics of the 'no-frost' evaporator are discussed. Two methods for improving 'no-frost' evaporator performance are also presented. The second part focuses on the defrosting research, including an analysis of defrosting energy consumption. Four methods to improve refrigerator defrosting performance are also summarized. Suggestions are made for future research initiatives and essential improvement measures. This review strives to serve as a valuable reference for the study of frosting and defrosting characteristics in household refrigerators and freezers. [ABSTRACT FROM AUTHOR]

• Climate change's impact on retrofitting was tested on Le Corbusier's apartment. • Climate change scenarios project up to 70% increase in summer overheating. • Adaptive natural ventilation can reduce warm discomfort of 50–60%. • Adaptive setpoints can reduce total energy consumption of 20–30%. • Adaptive strategies are particularly favourable in historic buildings. Historic buildings, often exempt from retrofits to preserve originality, require retrofitting due to potential future climate-related indoor issues. For Modern Architecture constructions, there is a need to find solutions that help address the discomfort and heat losses caused by the extensive use of glazing. By analysing Le Corbusier's studio-apartment this paper aims to inspect: (i) Solutions for energy, Indoor Environmental Quality, and preservation; (ii) Climate change impact on them; (iii) Possible adaptation strategies to cope with climate change. Spectrally selective glazing reduced summer overheating by 15%; high-performance glazing lowers cold discomfort (24%) and heating (22%) but increased warm discomfort; shadings maintained energy use and reduced summer discomfort by up to 44%; thermal insulation can reduce winter discomfort and halves energy use but raised summer discomfort by up to 41%. Tests on 2050–2100 climate change scenarios reveal up to 70% more warm discomfort hours. Adaptive strategies can help, with natural ventilation cutting warm discomfort by 50–60%, and adaptive setpoints reducing cooling demand by about 35%. Adaptive strategies can complement traditional retrofitting in addressing current and future climate conditions, especially in historic buildings that require minimal interventions to preserve their historical characteristics and enhance resilience. [ABSTRACT FROM AUTHOR]

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

In a context of massive renovation of residential housing, stakeholders need decision-support tools based on knowledge of the current building stock and an accurate simulation of energy demand. For this purpose, we developed a val- idation/calibration method on a territorial/national scale in order to represent the real consumption of housing. This methodological approach provides (1) more reliable identification of energy-saving measures (changes in technology or behaviour) and (2) improved knowledge of the energy simulation tool and its post-calibration performance for optimisation issues. The main contribution of the calibration method described in this paper is the geographical scale concerned: all French residential housing has been modelled, simulated and calibrated with national data (geometries and attributes) on buildings. Furthermore, some occupants' socio-professional characteristics have been taken into account to reflect their actual energy behaviours. This is different from traditional approaches that focus only on a few buildings or archetypes. This paper also describes the application of this methodology on an Open Source simulation software in order to be easily verifiable and usable. This linear model will also be used to optimise renovation solutions at territorial scale in future work. All data used in this paper are Open Data and thus available to the scientific community. This method enabled more than 18 million buildings to be calibrated while reducing the Normalized Root Mean Square Error, between simulated and real energy annual consumption, from 52% to 24% for gas and from 24% to 15% for electricity. In addition, the user of the method is free to prioritise either the maximum error reduction or the number of calibration coefficients if a simpler model is desired. This paper also discusses the results obtained from this method for future improvement. [ABSTRACT FROM AUTHOR]

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

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

• The paper reviews 33 on-field residential feedback studies. Only occupied homes where feedback was provided and energy consumption was monitored were considered for the review. • Our study proposes a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style and mode of access. • Understand the different ways in which field studies were conducted and assess a few parameters to establish their relationship with energy savings achieved. Residential energy feedback is about providing personalized information on household energy use to consumers to encourage energy savings. This paper conducts a review of field-based studies that have evaluated the impact of energy feedback on residential energy consumption. The review includes studies in real occupied homes that have deployed feedback intervention(s) and measured energy savings. Our study builds a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style, and methods of access. Energy savings from similar feedback types were found to differ depending on how the study was conducted. The reviewed studies deployed a range of feedback information including energy units, energy cost and tailored information conducted across diverse audiences (ethnicity, geographical positioning), varying experimental types (longitudinal, Randomized Control Trial) and, size and duration of the studies. The duration of studies varied widely, ranging from one month to three years and demonstrated energy savings between 5% and 20%. While most studies achieved energy savings due to energy feedback, a few of them reported an increase in energy consumption which could be due to rebound effect. Most of the studies in literature adopted a longitudinal design where the current household energy consumption was compared with the past consumption to arrive at the savings. Others used Randomized Control Trial (RCT) design where energy consumption of intervention group was compared with control group. Most of the studies were conducted in developed countries with cold climates.. There are fewer studies done on large-scale residential energy feedback especially in emerging economies where there is a rise in air conditioning use. We propose future studies to also consider the human behavior and cultural influences while evaluating impact of energy feedback. Our recommendation is that the academic and policy community address this gap since energy feedback is likely to stimulate positive behavior change amongst householders leading to energy savings. [ABSTRACT FROM AUTHOR]

The growing awareness of climate change is constantly moving the attention of designers and policymakers from typical and current scenarios to future layouts. This new approach introduces a degree of uncertainty that should be accounted in the building design process. The multitude of possible scenarios suggests considering dynamic system that can adapt themselves to unpredicted operating conditions. The aim of this study is to test a new approach and a new flexibility metric analysing the behaviour of a high-performance dynamic internal curtain in current and future scenarios. The first part of the paper focuses on the optimization of the dynamic system in the current scenario; this preliminary analysis represents the traditional design approach and is considered the baseline for all the comparisons. Hence, defining a matrix of likely scenarios, this paper explores the behaviour of three different selected control strategies – always off (i.e., no curtain), fixed control, optimized control – in the different scenarios considering possible variations of i) climate, ii) urban context, iii) internal loads, and iv) building use. The main outcomes of the research are, on the one hand, the comparisons of the control strategies and the benefits of the dynamic system and, on the other hand, the definition of a new metric that can properly describe the flexibility of the building systems with reference to the future scenarios analysed. This metric is based on six classes – which describe the statistical distribution of the energy consumptions of all the scenarios simulated in the matrix – and an index called Energy Flexibility Index (EFI) that quantifies the flexibility of the technology considered. The case study analysed highlights how implementing a dynamic shading system, considering the same building use, can increase the EFI of nearly 21% for non-optimized control strategy and up to 23% when optimized. While changing the building use in residential reduces these values respectively, to 6% and 7%. [ABSTRACT FROM AUTHOR]

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

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

Traditional air-conditioning systems are energy intensive and contribute to higher operational costs especially in summer. This paper examines and compares the performance of two cooling systems in a room: one with a cooling tower connected thermally activated building system (TABS) combined with a fan coil unit (FCU), and the other with a conventional inverter split air-conditioner (AC). Natural ventilation adversely affected indoor air temperatures in TABS room while integrating FCU operation reduced the temperature by 5 °C and AC provided the most comfortable temperature. Thermal comfort survey of three occupants showed significant inconsistencies with Fanger model particularly for TABS with FCU and TABS without ventilation cases, as occupants felt comfortable indicating the positive effect of only sensible cooling. Energy consumption studies show that TABS in closed condition and with natural ventilation as the most energy efficient with AC being least efficient of the four systems. In terms of overall comfort and energy consumption for continuous operation throughout the day, TABS integrated with FCU was deemed the best option. [ABSTRACT FROM AUTHOR]

• The energy consumption can be effectively predicted by the prediction model. • Spearman correlation coefficient is used to screen input parameters of predicted model. • The prediction model with optimal performance requires only three input variables. • The prediction model has better results by using learning rate of 0.02 in training. • The energy consumption at a certain indoor temperature setpoint can be predicted. With increasing energy consumption, how to achieve the energy-saving operation of air-conditioning systems is crucial for improving the energy efficiency of buildings. The accurate and reliable energy consumption prediction of air-conditioning systems can be useful for optimizing the energy supply and equipment operation strategies. However, most existing studies focus on the prediction of the long-term energy consumption of air-conditioning systems, which usually exceeds the individual control time steps of the air-conditioning system. If the energy consumption of air-conditioning systems in the next control step can be predicted, then the least energy-consuming operating strategy can be selected at the beginning of each control step by relying on the predicted energy consumption, thereby achieving greater energy conservation. This paper proposes a real-time energy consumption prediction model for air-conditioning systems based on a long short-term memory neural network. The correlation between the energy consumption of air-conditioning system and input variables can be obtained by calculating the Spearman correlation coefficient. The predicted model results show that the normalized root mean square error of the prediction model is 0.0429 when the indoor air temperature, condenser inlet temperature, and historical energy consumption are set as inputs to predict the energy consumption of the air-conditioning system for the next 15 min. Meanwhile, the energy consumption at a certain indoor temperature setpoint can be predicted by the prediction model to aid in selecting a more appropriate indoor temperature setpoint, which can reduce energy consumption by optimizing the operation strategy of the air-conditioning system at each control step. [ABSTRACT FROM AUTHOR]

• An optimization algorithm is developed for determining optimal flexible workplaces. • The thermal comfort models in the objective function are learned from personal data. • The optimization allows a customized trade-off between thermal comfort and energy consumption. • The algorithm is verified using both collected data and the ASHRAE database. Due to distinct preferences across individuals, a large proportion of people are not satisfied with the thermal environments of their workplaces. Although recent studies have investigated flexible workplaces to meet the different preferences of individuals, the feasibility of adopting them to real-world environments is limited by the room capacity and potential for extra energy consumption. To address the knowledge gaps, this paper proposes a framework to jointly optimize thermal comfort and energy consumption of buildings through flexible workplaces by considering personal thermal preferences. A joint optimization algorithm that integrates building energy and personal thermal comfort models is developed based on the concept of the Large Neighborhood Search (LNS) algorithm. Analytical energy prediction models are obtained through Nonlinear Polynomial Regression (NPR) and personal thermal comfort models are established using Support Vector Machine Models (SVM). To verify the algorithm, a case study considering two scenarios (with and without existing personal thermal comfort data) is presented. The results indicate the proposed optimization algorithm can significantly improve the thermal comfort of the occupants while saving 22% and 14 % of energy consumption in the two scenarios, respectively. The proposed optimization method is valuable for building managers while adopting the concept of flexible workplaces. [ABSTRACT FROM AUTHOR]

With rising energy prices, it is becoming increasingly attractive for households to form an energy community (EC) and become an active member of the energy system. Therefore, the choice of the right sharing coefficient, which defines how the produced energy is shared, is important for the expected profits. The aim of this paper is to analyse and compare two existing sharing coefficients with two new sharing coefficients. Therefore, we define two EC setups, one with only residential members and one with residential and commercial members, and investigate the impact of the sharing coefficients by using a data driven simulation model. For the analysis, we use a Monte Carlo approach in combination with load profiles of EC members simulated via time series models to account for random variation in electricity demand. The applied methodology gives additional insights on the profitability and the distribution of savings. The analysis shows that sharing coefficients that benefit small consumers are essential to incentivise private members to form an EC with commercial members. It is shown that when such an EC is formed, higher savings, a higher degree of self-sufficiency and a more efficient use of the produced electricity can be achieved. [ABSTRACT FROM AUTHOR]

• Energy Communities analysis and review in Europe and, more specifically, in Spain. • Promoting energy poverty reduction and cooperation through Energy Communities. • Real case study focused on reducing energy poverty in the city of Getafe (Spain). • Selection of public buildings to implement energy community in two neighborhoods. • Significant electricity bill reductions for energy poverty households. Advancements in renewable energy technologies, a growing awareness of the need for sustainable energy solutions, and the emergence of new business models in the energy sector, have contributed to the growth of Energy Communities. Public entities can find in energy communities today an opportunity to tackle energy poverty, to increase the cooperation among citizens and to empower them to be a more active player within the energy sector. This paper analyzes energy communities in Europe and focuses specifically on Spain; considering the three beneficial aspects previously mentioned. It aims to obtain ground truth about the applications of these new citizens' arrangements. A citizen-led initiative at the city of Getafe (Madrid, Spain) is included and discussed in detail. This case study focused on reducing energy poverty of its participants. An energy community was designed and implemented as a collective PV solar self-consumption infrastructure. The roofs of various public buildings in the two case study neighborhoods were selected for locating the PV installations. Real metered data were used to evaluate the solution. A public building was selected for the energy community, considering generation on roof and dwellings proximity. The results provided that out of the 77% PV generations could be used to contribute to the demand of 100 residences; while the resulting 23% would provide approximately 60% of the energy demand of the public building. The setting up of the Energy Community not only had the technical part but also included workshops seminars and information sessions, to ensure that people prone to benefit and maximize its impact. [ABSTRACT FROM AUTHOR]

• A new spray cooling system is proposed based on PV/T and heat recovery in sow houses. • A simulation model is developed to dimension the new system. • The maximum error is less than 7.2% for the key equipment models in the new system. • The optimal operating ranges of new system are determined in five climate regions. • The operating control suggestions of new system are obtained in five climate regions. In this paper, a new spray cooling system (NSCS) is proposed based on PV/T and heat recovery in sow houses. It contributes to improving system applicability in high-humidity areas and reducing system energy consumption due to solar dehumidification and heat recovery. Meanwhile, the simulation model of NSCS is established and the reliability is verified for the models of its key equipment. The results show that the maximum error is less than 7.2% for the models. The optimal control parameter ranges are further determined for NSCS in five climate regions in China through simulation. The results show that in hot-summer and warm-winter (HSWW) region or hot-summer and cold-winter (HSCW) region, the optimal operating ranges are 40–45 kW and 7245–9245 kg/h for spray power and mass flow rate of circulating water, respectively. However, they are 25–30 kW and 13245–15245 kg/h in other three climate regions, respectively. Finally, the annual performance parameters are compared for NSCS in five typical cities. The regional applicability and operating control suggestions are further obtained for NSCS in five climate regions. The results show that the heat recovery coefficient and energy efficiency ratio are the maximum when NSCS is operated in HSWW and HSCW regions. In high-temperature and arid regions, removing desiccant wheel from NSCS may be a good method to reduce system investment and energy consumption. Meanwhile, the surplus electric and thermal energy can be used for regional energy utilization in NSCS. The above conclusions can determine the regional applicability of NSCS and guide its operating control in different climate regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

[Display omitted] • An architect-friendly rapid evaluation for the adaptive facades (AFs) is presented. • The method eliminates uncertainty in AFs selection during the early design stage. • Optimal hourly values are obtained via co-simulation with EnergyPlus and Python. • AFs strategy is characterized by climate and optimal values using a white-box model. • U-value strategy reduces consumption by up to 6.6%, SHGC&τ v strategy by 17.0%. Adaptive façades (AFs) are technologies with great potential to reduce energy consumption by changing their properties to adapt to variable climatic conditions. This paper proposes an architect-friendly computational approach for the exploration of strategies for the selection of the thermal parameters of AFs based on co-simulation and white-box models in the early design stage. In the case study, the proposed approach is applied to obtain strategies with variable U -values and variable SHGC& τ v values for high-rise office buildings in seven cities. The energy consumption analysis of five scenarios is carried out to investigate the energy-saving potential of the AFs and to propose design strategies for transparent façades, and the accuracy of the approach is verified. It is concluded that the highest match of the variable U- value strategy with the optimal hourly U- values is 85.3%, while that between the variable SHGC& τ v strategy and the optimal hourly SHGC& τ v values is only 71.0%. Regarding the energy-saving potential, the highest energy-saving rate is 6.6% for the variable U- value strategy. However, for the variable SHGC& τ v strategy, the highest energy-saving rate is greater than 10.8% in seven cities. The difference between the energy-saving rates of the proposed strategy and the optimal hourly strategy is less than 0.4%, and it is 0.8% for the AFs with variable U- values and variable SHGC& τ v values, respectively. This architect-friendly approach represents a widely used design strategy for AFs that can directly provide recommended thermal parameters based on climatic conditions, and is an effective way to demonstrate the potential application of AFs in the early design stage. [ABSTRACT FROM AUTHOR]

• The paper explores the construction method for compact heterogeneous ensemble models. • The paper adopts the exhaustive search method for optimal model identification. • The paper proves the necessity for optimal model identification. • The paper analyzes the benefits and limitations of the exhaustive search method. Heterogeneous ensemble learning has received increasing attention in building energy prediction research because it provides more stable and accurate predictions than the conventional single model-based methods. Although existing studies have proved the feasibility of adopting heterogeneous ensemble learning in building energy prediction, they have not fully explored the method of constructing an optimal heterogeneous ensemble learning model based on limited base model resources. This paper attempts to identify the optimal heterogeneous ensemble learning model for building energy prediction by using the exhaustive search method. Six machine learning methods are used as the alternative base model for constructing heterogeneous ensemble learning models. All 57 heterogeneous ensemble learning models that can be built from the six base models are compared and the optimal model is identified. The hourly building energy prediction experiment is performed on an institutional building located at the University of Florida to verify the proposed exhaustive search method. Three modules representing different semesters of the test building are developed and tested to examine the predictive performance of the proposed heterogeneous ensemble learning models. The research findings demonstrate the feasibility of the proposed exhaustive search method in identifying the optimal heterogeneous ensemble learning model. The optimal heterogeneous ensemble learning model found in this study can reduce the MAPE by 4.9% − 6.6% compared with its most accurate base model in the model testing stage. The experimental results and research findings provide critical guidance for applying heterogeneous ensemble learning to data-driven building energy prediction. [ABSTRACT FROM AUTHOR]

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

[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

• Design criteria for active solar technologies integration in the historic built environment to favor energy transition, cleaner energy production, and climate mitigation. • Comparison of international guidelines' recommendations, design and assessment criteria. • Common glossary for the integration of active solar technologies in cultural heritage. • Definition of clear and updated design criteria valid for traditional and innovative active solar systems. • Support to solar innovation for the development of ad hoc products that considered correct design criteria. The paper focuses on architectural integration of active solar technologies (photovoltaic and solar thermal systems) in the historic built environment. Interpreted and used as a building material in the envelope, they are able to positively support energy transition and to contribute to climate change mitigation without compromising the original typological-functional characteristics. Their integration is fostered by innovative architectural design, aesthetical appeal, flexibility, multi-functionality, cost reduction, and technological development. Moreover, they can enhance economic value, functionality, and human comfort of buildings. Despite these advantages, there is a social diffidence on their acceptability due to policy, design, technical, economic and information barriers. To overcome these issues, the paper aims at identifying shared design criteria for their integration in the historic built environment, through a detailed assessment of 44 international guidelines. The study develops a taxonomy of international recommendations on energy efficiency, microgeneration, and solar integration, missing in literature so far, by analysing and comparing recurring criteria and recommendations. Also, a glossary of the most important terms according to the aesthetic, technical, and energy integration levels is created. Thus, the research defines an innovative framework of reliable design criteria, and a shared vocabulary to promote active solar technologies integration in the historic built environment. [ABSTRACT FROM AUTHOR]

• Five different energy efficiency alternatives for the building were studied. • A ground-source heat pump and solar PV usually provide the highest profitability. • A household battery has become profitable at the 2022 electricity price. • Dimensioning of a solar PV system massively depends on the electricity price level. • At the 2022 electricity price level, the array-to-inverter ratio can be up to 4.9. Buildings and the construction sector make up about a third of the final energy consumption and energy-related carbon dioxide emissions, offering a significant potential for emission reductions. This paper investigates the conversion of a Nordic oil-heated townhouse into a carbon-neutral one. Five different energy efficiency (EE) alternatives including, for example, different heat pump types and their dimensioning are studied. The electricity demand of the EE alternatives is aimed to be covered as cost-effectively as possible with an on-grid system based on solar photovoltaics (PV) and a battery energy storage system (BESS). The cost-optimal dimensioning of the components is based on the 30-year life cycle cost (LCC), and the work is carried out using an hourly level simulation model developed in MATLAB. To maximize the profitability of the BESS, an intelligent control algorithm based on linear programming is implemented based on a day-ahead electricity spot price for the years 2020–2022 and "perfectly" forecasted power demand and generation data. The data used in the simulation consist of measured electricity consumption, simulated heat generation based on annual oil consumption, and simulated solar PV power generation. It was observed that an about 60% partial-power-dimensioned ground source heat pump together with a solar PV system provides almost always the lowest LCC. However, the BESS was shown to be profitable at the current investment costs for the first time at the 2022 electricity prices, enabling a capacity of 210–230 kWh. On average, the cost-optimized on-grid electricity system was found to reduce the 30-year life cycle costs by 6%, 20%, and 85% with the electricity prices of 2020, 2021, and 2022, respectively. When the price of electricity is exceptionally high, it may be justified to implement more extensive energy efficiency improvements, such as additional insulation of the building envelope. Nevertheless, the impact on the life cycle costs is marginal. In addition, the cost-optimized solar PV capacity has increased more than sevenfold between 2020 and 2022. [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]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models. [ABSTRACT FROM AUTHOR]

The low energy efficiency of the European building stock is both a social and environmental issue. However, plans and studies on energy retrofitting interventions do not usually evaluate the impact of these measures on households' energy affordability. This paper analyses and compares the effect of alternative retrofitting strategies on thermal energy services' affordability in Spanish vulnerable households. This analysis was carried out by applying the Required Thermal Energy Expenditure model proposed in previous studies to several 2030 residential building stock scenarios, setting 2020 as a baseline. Therefore, this work proposes a methodological approach to assess the impact of the considered energy efficiency interventions on energy poverty in winter (WEP) and summer (SEP) and carries out both a 'social cost' and a cost-benefit analysis. The dwelling's thermal-enclosure retrofitting stands out as a very WEP-reduction and social-cost effective measure. Instead, replacing old thermal systems with more efficient ones would have a higher impact on SEP. Combining both kinds of retrofitting measures could reduce heating and cooling required expenditures by, respectively, 49% and 59%, and decrease the WEP and SEP share, correspondingly, by 35% and 63%. These results might inform policymakers to enhance the targeting and design of energy efficiency programs. [ABSTRACT FROM AUTHOR]

• Different renovation intervention levels are evaluated. • The technoeconomic feasibility by itself does not explain the actual renovation rates. • Existing social, material and legal barriers are key for a comprehensive analysis. • Measures should be taken to boost district/building level interventions. Building is one of the main sectors in which significant energy savings can be achieved with a consequent reduction in emissions. This paper assesses the large-scale renovation of buildings, exploring the quantitative and qualitative factors that determine their replicability potential from three different perspectives, namely dwelling, building and district intervention level. Different passive and active measures are assessed, covering energy saving measures, the improvement of energy supply systems and the integration of renewable energy sources. Different scenarios are defined for each intervention level, which are evaluated using the methodology developed in the IEA-EBC Annex 75 project. The methodology is applied to a residential district located in Bilbao (Northern Spain). The analysis is based on the results obtained from the simulation of 41 combinations of different renovation options, obtained by simulation in the Design Builder software. The assessment is carried out from the evaluation of different key performance factors, including annualised cost and annual primary energy consumption, as well as the CAPEX and OPEX requirements. The results show that energy renovation of buildings offers a great opportunity for energy reduction at affordable investment costs, obtaining the cost optimal values when the intervention focus on the energy system and reaching net NRPE values close to zero when the intervention is carried out in a comprehensive way, considering energy systems and thermal improvement of the envelope. Additionally, the different energy reduction scenarios show that, while interventions at district level offer the greatest potential for minimising annual costs and primary energy consumption levels, intervening at building level offers only slightly worse results. However, there are other issues which can better explain the current low rate of renovation works in the urban environment. Thus, if the additional existing social, material and legal barriers and constraints are included in the analysis, it becomes clear why renovation from a district perspective is not actually leading the race. Given this situation, some measures and policies are proposed to realise the true potential of large-scale building renovation. [ABSTRACT FROM AUTHOR]

In recent years, water-side free cooling has been broadly applied in data centers to lessen the high cost of energy consumption. The energy-saving opportunities of free cooling have been recognized by numerous studies across the world. In this paper, the field test and energy performance evaluation were performed in an operational data center with water-side free cooling, located in Chongqing, China. Existing problems were found during tests, including the insufficient cooling capacity of towers in wintertime and the inability to activate partial-free cooling. The results of the tests show that the reduction in Power Usage Effectiveness (PUE) is much lower than the expected. To understand the marked discrepancies, the performance of the cooling tower was discussed in terms of the approach temperature and actual cooling capacity. The maximum cooling capacity in winter is only about 50 % of the rated. With the consideration of the cooling tower configuration performance in different climate conditions, the conservative energy effects were calculated in 34 provincial cities of China. To prolong the hours available, optimized operation schemes of cooling towers are presented. The available hours for free cooling could be increased from 730 h to 1300 h when operating at 67 % of rated flow rate, resulting in a decrease in the energy saving of chillers between 8.44 % and 21.05 %. [ABSTRACT FROM AUTHOR]

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

• Propose a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of cooling load and outdoor wet-bulb temperature. • Obtain the optimal match of prediction models and optimization algorithms for improving the performance of the MLB-MPC. • Investigate the impact of disturbances on the performance of global optimization. • Achieve an energy-saving ratio of 7.1% for the air-conditioning system when compared with the measured operation data. The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs. [ABSTRACT FROM AUTHOR]

• EV presents a technical challenge to the grid and an opportunity to introduce sustainable and novel solutions. • A sustainable energy strategy is proposed to exploit the synergy between EV batteries and heat pumps. • Electrification of transport and heat in buildings can reduce typical householder carbon footprint by up to 87%. The demand for electricity has been increasing worldwide and it is predicted that this trend will be particularly reinforced in developing countries by the gradual electrification of the transport sector and heat generation in buildings. The energy supply infrastructure required to meet the additional electricity demand should be carefully managed in light of the climate change carbon emission targets and commitments. A large proportion of the new electricity loads will be met from renewable sources. Therefore, means of power storage become vital to smooth out the intermittent nature of these energy supplies. The introduction of Electric Vehicles (EVs) could provide a viable and dynamic power storage solution through the concept of Vehicle-to-everything (V2X). This involves the storage of renewable energy (RE) in EV batteries during the charging cycle and restitution to the grid (V2G) or homes (V2H) when needed. In this context, this paper presents a methodology involving several strategies to stabilise the grid system and examines the impact of various types of EVs and heat pumps (HPs) for supplying heat in buildings. The results of this research approach show that the synergy of using V2H could reduce the carbon footprint of a typical domestic building in the United Kingdom (UK) by up to 87% and potentially recover up to 21.9 kWh/day of surplus renewable energy. [ABSTRACT FROM AUTHOR]

Glued Laminated Bamboo (GluBam) has been innovated under the support of modern industrial technology, and this renewable material can drive the development of building structural system in a fashion of low carbon footprint. From the perspective of carbon emission, energy consumption and cost-based economy, this paper presents a comprehensive life cycle assessment (LCA) of steel-GluBam hybrid truss and virtually designed steel truss for a comparison. A steel-GluBam hybrid truss constructed at the Nanjing Tech University is used to carry on this study. The system boundary (i.e., production phase, transportation phase, construction phase, operation phase and disposal phase) of life cycle are fist determined. The mechanical properties of the truss structure are considered as the functional equivalence. The inventory of construction materials and the elements in each phase in the life cycle is compiled from the SimaPro and the available literature/specification. Based on the inventory analysis, the Monte Carlo method is employed to conduct the uncertainty analysis of carbon emission of the truss. The assessment results show that the life cycle carbon emission, energy consumption and cost of the steel-GluBam hybrid truss are equal to 4572.5 kgCO 2eq , 71.15 GJ and 15626.6 CNY, respectively. The production phase is the largest contributor to the carbon emission, energy consumption and cost, and the following one is the operation phase. Whereas the transportation phase, the construction phase and the disposal phase together contribute to only the small parts of emissions and cost. GluBam has the function of carbon sequestration leading to the reduction of carbon emission and energy consumption in the production phase, and other phases have no advantage on the emissions and cost of GluBam. The 95% confidence interval of life cycle carbon emission based on the Monte Carlo simulation is estimated as [4290,6370] kgCO 2eq for the steel-GluBam hybrid truss. With respect to the whole life cycle, using GluBam to replace the steel pipe in the upper chord pole and the diagonal web pole of the truss has a little larger cost but its environmental impact and energy consumption reduce a lot. The railway transportation of GluBam reduces 36.5 kgCO 2eq carbon emission, 0.68 GJ energy consumption and 83.7 CNY cost in the life cycle with reference to the road transportation. The steel-GluBam hybrid truss optimized based on the functional unit occurs in the case of reducing the cross-section of steel pipe from 42 mm × 4 mm to 32 mm × 2.5 mm. [ABSTRACT FROM AUTHOR]

• The new solution for the façade integrated photovoltaic system, En-ActivETICS was proposed and described in detail, including technical recommendations. • The calibrated numerical model was used to determine hygrothermal performance and identify the most crucial issues of En-ActivETICS. • The positive effect of PCM and diffusion channels was revealed based on a numerical study using calibrated thermoelectrical and hygrothermal models. • The En-ActivETICS was investigated considering local climatic and economic conditions among 38 European countries. • The roadmap 2030 for the En-ActivETICS advancement was developed and presented. The main goal of the European building market transformation regards the reduction of CO 2 emissions and energy demand in the built environment towards net zero carbon buildings. This drives research and building practice to search for economically justified solutions that contribute to achieving these objectives. The work presented in this paper is concerned with the technological and economic context. The original solution of photovoltaic integration with the external thermal insulation composite system named the En-ActivETICS was developed, tested and analysed. The energy potential of the En-ActivETICS across Europe, considering south oriented façade is from 88.3 to 195.8 kWh/(m2⋅a), with the efficiency of solar energy conversion of around 13–14%. The problem of system overheating was solved by applying PCM mortar beside the PV panel, which allowed to keep the PV temperature below 70 °C. The risk of moisture load (from driven rain or water vapour condensation) was avoided by diffusion channels pressed in the mortar. Furthermore, the economic analysis of system implementation in Europe is also promising. Considering the availability of solar energy and electricity prices around Europe the payback time of the En-ActivETICS varies between 12 and 44 years for analysed countries. According to the presented roadmap for the En-ActivETICS advancement a payback period of <10 years is possible with a maximum cost of the system <275 EUR/m2 and electricity price >0.25 EUR/kWh. [ABSTRACT FROM AUTHOR]

Building energy consumption forecasting is crucial for energy managers and policymakers. However, acutely predicting the energy consumption of any building is more important in energy efficiency benchmarking of buildings. Energy benchmarking is a precise technique for measuring, tracking, and reducing end-use energy usage of buildings by employing comparative scenarios. Bottom-Up energy consumption assessment has been used in several studies to evaluate a collection of buildings' energy performance to a standard. Traditionally, researchers have used a single approach to predict the energy use of buildings and to conduct benchmarking. However, it has been found that doing so frequently compromises the accuracy of the predicted energy use of buildings. It also has an adverse effect on the buildings' energy benchmarking framework results. This paper will provide an energy benchmarking framework for residential buildings based on the blending techniques of multiple approaches. Over 3000 datasets were gathered from the Indian city of Jaipur. In order to analyse the trends in energy consumption, the data were divided into categories based on economic levels. The data is then subjected to some fundamental statistical analysis, along with several approaches are blended to forecast the energy consumption of the buildings with the highest accuracy. These benchmark levels set guidelines for assessing and recognising strong performance while also identifying underperforming buildings and giving them more priority for energy efficiency improvement. The technique may be used for different metropolitan levels and climatic conditions. [ABSTRACT FROM AUTHOR]

• Households change their heating patterns when they switch to a heat pump. • Typically, a setback temperature 2–5 °C lower than the desired thermostat setting. • We calculate the increase in heat demand with varied parameters. • The increase in heat demand is determined by heat loss and thermal mass. • This would be a useful metric for 'heat pump readiness'. Heating with air source heat pumps is an important step in the transition to Net Zero Carbon for many households. When predicting the resulting energy consumption it is frequently assumed that heating demand is unchanged from before the switch from gas heating. However, heating patterns usually change, with a net increase in heating demand to achieve the same level of thermal comfort. This paper discusses the change in pattern and uses modelling to determine the resulting increase in energy demand. It shows that the increase can be predicted with information about dwelling heat loss and thermal mass as well as weather and the pre and post switch heating pattern. The increase can be 20% or more for homes with high heat loss. It proposes that the metric for heating demand increase would be a useful measure of heat pump readiness, and that the parameters required to assess this should be provided on energy performance certificates. [ABSTRACT FROM AUTHOR]

• Long term field evaluation of forecast control of heating. • Easy integration with existing installations in buildings. • Widely applicable measure for energy savings around 10%. • Payback time below one heating season. The priority for reaching carbon neutral buildings by 2050 is to first increase energy efficiency in existing buildings by use of easily and widely applicable measures that are verified based on long-term field research. One such solution is a forecast control of heating systems, which can be quickly and easily integrated to existing weather based controls. However, there are still no long-term field evaluation and detailed analyses of the main features of such a system, which would provide a clear picture about the system's energetical and economical features and thus increase their wide application in existing buildings. This paper provides the data from a long term field evaluation of an innovative forecast control of heating systems (forHEAT), which can be easily installed (in less than 2 h) and integrated with existing installations in millions of existing buildings in Europe and throughout the world. The energy consumption for heating was evaluated in seven residential buildings and three public office buildings before and after the installation and use of the forecast control. The resulting energy savings averaged 13.4% in the residential and 10.7% in the public buildings located in continental European climate, with very cold winters. The payback time averaged a 0.6 heating season and in most case studies does not exceed a single heating season. [ABSTRACT FROM AUTHOR]

Providing the occupants with tailored feedback on their energy consumptions can be an effective method to help the users to adopt energy-efficient behaviours. However, in specific contexts such as the social housing, this may be challenging, due to the peculiarities and the economic constraints of the end-users, which make them vulnerable and needing a proper design of the information. Nevertheless, there is a lack of detailed studies in the literature analysing the effects of personalized energy feedback on the space heating consumption and the indoor air temperatures in the social housing sector. This paper aims to evaluate the effects on space heating consumption and indoor air temperature of a personalized feedback campaign delivered in three social housing buildings consisting of 27 apartments in Mediterranean climate. To this end, a personalized feedback campaign was designed and developed, providing for the use of disaggregated energy consumption indicators to show the location of excessive thermal energy consumptions. The daily thermal energy consumptions, as well as the indoor air temperatures of the single dwellings, were monitored daily for over four heating seasons and their variations were analysed before and after the implementation of the personalized feedback. A saving in normalized natural gas consumption of the buildings was found on average equal to −14,8% after the feedback campaign, with no significant variations in the indoor air temperatures. However, some critical issues have been highlighted in the response of individual end-users to the feedback provided. A greater propensity of the occupants to regulate the indoor air temperature through the thermostatic radiator valves in the unoccupied rooms of the house was also observed after the occurrence of the feedback campaign. [ABSTRACT FROM AUTHOR]

• A mathematical model to calculate the LCOS for reversible fuel cells and Li-ion batteries. • Techno-economic model for a standalone PV solar system in green energy buildings. • Uncertainty and simulation cost analyses of energy storage systems used in green buildings. Energy storage systems (ESSs) were introduced to overcome the risks posed by energy curtailment. In this paper, we compare the Levelized cost of storage (LCOS) for PEM Reversible Fuel Cells, Solid Oxide Fuel Cells, and Lithium-Ion Batteries in three different locations in the United States (Tucson, Seattle, and Rochester) that are characterized by different energy consumption profiles. This research examines three commercial buildings of different sizes: small, medium, and large, with three occupancy profiles: low, medium, and high energy consumption in each of the three locations. The objective is to quantify the impact of uncertain demand and uncertain financial parameters on the LCOS. The results show that all ESSs can represent an attractive alternative to energy curtailment regardless of the building size or the occupancy profile. Further, the results show that fuel cells are a better fit for demand uncertainty as they are more resilient to changes and remain attractive despite that. The uncertainty analysis showed that the minimum LCOS was $0.049/kWh, and it was for a PEM-RFC in a small building in Tucson under high energy consumption behavior. Oppositely, the maximum LCOS was $0.679/kWh, and that was for a large building in Seattle under high energy usage occupancy as well. [ABSTRACT FROM AUTHOR]

• The solar louver was proposed to increase direct heat gaining for building. • Effects of blades' angle and characteristics on heat gain of solar louver was quantified. • Indoor temperature was higher and temperature field was more uniform with coupled solar louver. • By comparison with non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%. Promoting solar energy for heating is of great significance for achieving the national goals of "double carbon" goals. Increasing the heat gain of window space could improve the solar energy utilization in congested urban environments. The paper proposed a new heating type for active solar house by solar louver. Thermal effects validation on solar louver were studied by field-test and simulation analysis. A solar louver model was established on the platform of MATLAB and TRNSYS. The simulated results showing that heat gain of solar louver reached the largest when blades' angle was about 10°. Solar louver with high reflectivity and low thermal emissivity could capture more heat gain. The in-situ experiments of active solar house coupling solar louver were carried out in Ordos. From 24th to 29th Jan. 2022, all-day comparative experiments of thermal effects in actual winter-weather were conducted. The experimental results indicating that solar louver in maximum light transmission mode was preferred in active solar house. Compared with the condition of solar louver in overlapped light transmittance mode, maximum light transmission mode could obtain 17.7% higher heat gain. Indoor temperature was more higher and temperature field was more uniform. Moreover, by comparison with the non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%, which could reduce energy consumption of building. The main work here provided a theoretical basis for performance optimization of active solar house. [ABSTRACT FROM AUTHOR]

The rise of clean energy such as solar energy provides a new idea to optimize the energy structure, and Photovoltaic thermal (PVT) heat pump system is one of the mainstream development at present. The wrong sensor data will have a great impact on the control and efficiency of the whole PVT heat pump system. In order to deal with this situation, Virtual in-situ calibration (VIC) based on Bayesian inference and Markov chain Monte Carlo (MCMC) is applied to PVT system. This paper presents a generic sensor fault diagnosis and calibration method for building energy systems using a PVT heat pump system as an example. Firstly, the data generated based on the mathematical model of PVT system is used to test the feasibility of VIC in this system. The results show that VIC can well reduce the systematic error and random error of the sensor. However, with the further study, it is found that in the actual system, it is impossible to establish a mathematical model which can reflect the relationship between the sensors with high accuracy. The VIC may fail or even have worse calibration results. Therefore, in order to solve the above problems, this study suggests using sparse autoencoder model instead of mathematical model. The model proved to be more accurate and to reflect the interconnectedness of the sensors, which helped the implementation of the VIC. The calibration method based on sparse autoencoder (SAE) can calibrate not only a single sensor in the PVT heat pump system, but also multiple sensors in a local area. After sparse autoencoder Virtual in-situ calibration (SAE-VIC) calibration, systematic and random errors of all sensors can be effectively reduced, and the accuracy of sensor calibration can reach more than 90 %. [ABSTRACT FROM AUTHOR]

• An optimization strategy model of district energy system under the direct reduction/increase goal of power load is presented. • The grid can directly control the energy consumption strategy in the form of instructions. • The proposed goal pays a certain price, but a large additional flexible utilization coefficient is obtained. • The energy compensation coefficient of the multi energy coupling system is mostly negative. With the wide application of regional centralized cooling (heating) system in the world and the growing maturity of energy supply technology, the focus of system optimization gradually transforms from technical optimization to strategic optimization to attain the common goals of economy and energy saving. Aiming at the operation strategy model of power grid interactive district energy system based on time-of-use (TOU) price proposed in previous research, this paper proposes new objective equations and constraints to explore the strategic deployment of district energy system when accepting the power grid requirements to reduce or increase the power load directly to improve the economy and energy efficiency of centralized cooling (heating) system. The modified optimization strategy model is used to respond to sudden demand response or power accidents. The strategy model can be directly controlled by the power grid, and the TOU price is no longer the main factor affecting the formulation of the strategy. In the face of sudden demand response or power accidents, the operation strategy optimization model proposed in this paper can guarantee the energy supply demand of users and optimize the economic benefits and energy efficiency of the systems. The operation strategies under various optimization objectives can be obtained through the simple input of energy loss of the pipeline and user cooling or heating load. The proposed model is suitable for the load allocation of energy system with single energy supply form and for the strategy optimization of multi energy coupling system. The accuracy and validity of the proposed model under various optimization objectives are verified based on experiment and simulative research. [ABSTRACT FROM AUTHOR]

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China's air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents' sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents' age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper's findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances. [ABSTRACT FROM AUTHOR]