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There has been general recognition within the construction industry that there is a discrepancy between the amount of energy that buildings actually use and what designers considered that they should use. This phenomenon is termed “The Performance Gap” and is normally associated with new buildings. However, existing and older buildings contribute a greater amount of operational carbon. In response to the Performance Gap, CIBSE have developed the TM54 process which is aimed at improving energy estimates at design stage. This paper considers how the TM54 process can also be used to develop energy management procedures for existing buildings. The paper describes an exercise carried out for a university workshop building in which design energy use has been compared with the actual building energy use and standard benchmarks. Moreover, a sensitivity assessment has been carried out using different scenarios based on operation hours of building/equipment, boiler efficiency and impact of climate change. The analysis of these results showed high uncertainty in estimates of energy consumption. If carbon challenges are to be met then improved energy management techniques will require a more systematic approach so that facilities managers can identify energy streams and pinpoint problems, particularly where they have assumed responsibility for existing buildings which often have a legacy of poorly metered fuel consumption. [ABSTRACT FROM AUTHOR]

A large-scale diffusion of photovoltaics (PVs) is expected to disturb the voltage control in an electric distribution network. When PVs generate more electricity than demand requires, the electric voltage may exceed its legal limit, and the PV generation is suppressed by the power conditioner. This paper evaluates the extent to which residential energy management resources (EMR) can contribute to controlling the distribution line voltage and reduce the amount of PV generation suppression while considering the variety among households due to family composition and appliance ownership and the operation constraints due to operation time restrictions, occupants’ comfort and control method (manual control or automatic control). This paper estimates EMR that can be provided by a washing machine, clothes dryer, dishwasher, rice cooker, electric pot, air conditioner and heat pump water heater. EMR varies among households and accounts for 3%–63% of annual household electricity demand. Although 9.9% of PV generation is suppressed when all detached houses have installed PVs in the assumed residential community, the EMR is able to decrease to no more than 3.0%; this reduction potential decreases by 23% owing to the above-mentioned operation constraints. [ABSTRACT FROM AUTHOR]

One of the main problems for adopting smart thermostats is consumers’ expectations about smart thermostats. Generally, the interfaces in smart thermostats and smart devices in the energy market are designed without consideration for the expectations of the customers or are not adjusting their operation according to the consumers’ expectations and behavior. Moreover, the business model does not consider one part of the product as the main link between customers and value propositions. This study shows a structure for creating a smart interface based on consumers’ expectations and shows an extension of the CANVAS business model. Furthermore, the paper re-defines the term “smart” in electric devices as the ability to adjust the system in order to reach the expectations of the consumer and to save energy. This term is not only defined in terms of saving energy but also it includes the customer’s expectations. If the system is only smart to control the temperature and save energy, the customer’s expectations may not always be achieved; thus, the smart devices are not successfully adopted in households. The design is based on artificial neural networks and a fuzzy logic controller to achieve the consumer’s expectations. The results show the interface operation on each block. The classification systems have to operate to detect changes in the customer’s market segment. The fuzzy logic controller tries to reach the customer’s expectations in order to move the customer into the energy saving market. This expectation interface tries to motivate designers to focus on consumer’s expectations for smart devices in the energy market. Not only are the energy requirements important to design a smart thermostat, but also the consumer’s expectations to create a successful smart thermostat in the electric market. Smart thermostats can reduce the consumption of electrical energy, but they need to be accepted. This interface provides an effective manner to connect the consumer’s expectations with the consumption of energy. This papers shows a proof of concept about intelligent interfaces. [ABSTRACT FROM AUTHOR]

Energy poverty can be understood as the inability of a household to secure socially and materially the necessitated level of energy services in the home. This condition is described as either energy or fuel poverty and it has been recognized by the EU's so called “Third Energy Package”. The work in this field has identified a number of household-level factors that influence the likelihood of experiencing domestic deprivation, with particular emphasis being placed on understanding how education levels correlates to the phenomenon. In that perspective, and in order to study the dynamics of energy poverty in Greece, a questionnaire was prepared and distributed mainly by means of in-situ interviews and, in exceptional cases, in remote sessions. The evidence presented in this paper is based on a comprehensive study of 762 questionnaires mainly for Western Macedonia (43.6%) and Central Macedonia (42.6%), Greece. Using the quantitative data gathered, we measure the energy vulnerability concerning the quality of the housing–dwelling, the heating system, indoor thermal comfort and the net-income. The research is, to our knowledge, the first attempt to analyze residential energy, habitants’ behavior, and attitudes in the middle of the economic crisis in North Greece. It has been determined, that the middle class has become the most vulnerable class in that sense. A further scope of this paper is not only to present this data, which prove that domestic energy deprivation appears not only in Southern but also in Northern Greece, but also to discuss measures that can be adopted in order mitigate energy and social vulnerability of the middle class. [ABSTRACT FROM AUTHOR]

Trustworthy savings calculations are critical to convincing regulators of both the cost-effectiveness of energy efficiency program investments and their ability to defer supply-side capital investments. Today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of energy efficiency programs. They also require time-consuming data acquisition. A spectrum of savings calculation approaches is used, with some relying more heavily on measured data and others relying more heavily on estimated, modeled, or stipulated data. The increasing availability of “smart” meters and devices that report near-real time data, combined with new analytical approaches to quantify savings, offers the potential to conduct M&V more quickly and at lower cost, with comparable or improved accuracy. Commercial energy management and information systems (EMIS) technologies are beginning to offer these ‘M&V 2.0′ capabilities, and program administrators want to understand how they might assist programs in quickly and accurately measuring energy savings. This paper presents the results of recent testing of the ability to use automation to streamline the M&V process. In this paper, we apply an automated whole-building M&V tool to historic data sets from energy efficiency programs to begin to explore the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. For the data sets studied we evaluate the fraction of buildings that are well suited to automated baseline characterization, the uncertainty in gross savings that is due to M&V 2.0 tools’ model error, and indications of labor time savings, and how the automated savings results compare to prior, traditionally determined savings results. The results show that 70% of the buildings were well suited to the automated approach. In a majority of the cases (80%) savings and uncertainties for each individual building were quantified to levels above the criteria in ASHRAE Guideline 14. In addition the findings suggest that M&V 2.0 methods may also offer time-savings relative to traditional approaches. Finally we discuss the implications of these findings relative to the potential evolution of M&V, and pilots currently being launched to test how M&V automation can be integrated into ratepayer-funded programs and professional implementation and evaluation practice. [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]

• 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]

[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]

[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]

• 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]

Green roofs have been recognized as an effective way to increase urban green space and improve building micro-environment. This paper investigated the thermal and energy performance of an extensive green roof under free-floating 1 1 Free-floating here means no heating or cooling system affecting the internal conditions of the room. and air-conditioned conditions in Shanghai, a coastal city with hot and humid summer. A field experiment was conducted on two full-scale rooms of a building from 30/07/2014 to 31/08/2014. One room was covered by a green roof while the other one was covered by a common roof. The results show that solar radiation has the strongest correlation with green roof’s cooling effect, and the correlation between solar radiation and heat flux through green roof is negative in free-floating condition but positive under air-conditioned condition. Outdoor temperature is the most correlated factor of green roof’s temperatures but the least correlated one for green roof’s cooling effect. Energy balance analysis indicates that evapotranspiration and long wave radiation dissipate most of heat gain for green roof, and soil water content has a significant impact on energy balance. This paper presents the basic thermal characteristic of extensive green roof in Shanghai and also make it possible for further validation of hygrothermal transfer model of green roof system. [ABSTRACT FROM AUTHOR]

This paper addresses the potential of multi-objective optimization (MOO) in conceptual design to help designers generate and select solutions from a geometrically diverse range of high-performing building forms. With a focus on the long span building typology, this research employs a MOO approach that uses both finite element structural modeling and building energy simulations simultaneously to generate optimized building shapes that are not constrained to regular, rectilinear geometric configurations. Through a series of case studies that explore performance tradeoffs of enclosed arches and static overhangs in different climates, this paper shows how MOO can yield architecturally expressive, high-performing designs, which makes the process more attractive to designers searching for creative forms. It also provides new insight into specific design responses to various climatic constraints, since optimization that considers both structure and energy can shift best solutions in unexpected ways. Finally, by displaying performance results in terms of embodied and operational energy, this paper presents new data showing how considerations of structural material efficiency compare in magnitude to total building energy usage. Together, these three contributions can influence current sustainable design strategies for building typologies that have significant structural requirements. [ABSTRACT FROM AUTHOR]

A green roof, a vegetative layer grown on a building roof, has become increasingly popular in recent years due to its environmental benefits. A large number of research studies have been conducted to evaluate the performance and benefits of green roofs through experiments and modeling analysis. However, only a few studies in the past literature addressed the energy performance of green roofs in cold climates. This paper, therefore, aimed to fill this gap by evaluating their energy performance in cold climates through experiments and modeling. The test bed was the green roof located on the roof of Schleman Hall on the Purdue University campus in West Lafayette, Indiana. It is equipped with a data logger and various sensors for measuring soil temperature, water content, etc. The experiment presented in this paper focuses on the thermal performance of green roof in cold climates and the impact of the phase change of water in the growth medium on the thermal performance of green roof. A transient numerical thermal model was developed and validated by the experimental data. The paper provides new insight of the impact of the phase change of water in green roofs on their thermal performance as well as their thermal characteristics. [ABSTRACT FROM AUTHOR]

The paper provides an overview of a part of research related to the analysis of information technologies and measures for improving the energy efficiency in urban planning that can contribute to mitigating the impact of climate change. The first part of the paper deals with problems supported by the numerous data on negative effects of climate change on the cities, such as urban heat islands, energy and ecological crisis. The second part of the paper proposes solutions that rely on information technologies which facilitate process of urban planning. Two projects related to the implementation of measures for improving the energy efficiency in urban planning were conducted within this research. The first one is considering the design of a spa centre in an undeveloped mountain landscape and the second one is dealing with reconstruction of a residential block in urban built environment. In the first case study, the proposed solutions rely on the information technologies, while in the second case study they rely on the implementation of measures for improving the energy efficiency in urban planning. The results show that the application of different measures in urban planning depends on the level of the built environment. [ABSTRACT FROM AUTHOR]

Global trends in reapplication of wood re-application and wood-based products, used as construction and architectural coating materials, are present not only because of esthetic, artistic and design requirements or tradition and nature-based inspiration, but also because wood adapts to modern trends in sustainable development. This paper presents traditional influence on application of wood as a façade cladding element in modern architecture of Serbia and provides the analyses of functional and design potentials of wood as an element of ventilated façade systems and its influence on energy efficiency. The objective of this research was to provide a more detailed insight into potentials and limitations of the application of wood and wood-based products, as façade cladding elements used for construction and reconstruction of architectural objects, in the climate conditions of Belgrade (Serbia) as well as to point out certain suggestions about improving their energy efficiency characteristics. The paper presents the examples of various methods of applying wood and wood-based products as elements of façade cladding in modern architecture of Belgrade. The main focus is put on an object that underwent different refurbishment methods and façade system alterations in order to meet the requirements of energy saving. [ABSTRACT FROM AUTHOR]

Nowadays, aviation industry has been faced with technology challenges and safety requirements. Therefore, aircraft manufacturers give special attention to environmental building capacities regarding to passenger comfort and safety. This paper aims measuring safety and comfortability level of passenger accommodation during the flight. Since the environmental and safety issues could affect humans on a different level significance, this paper is based on the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method as a decision making tool for balancing those two inputs. With the aim of defining rank of existing world market aircraft the Saaty scale was used for developing the weight of different criteria whilst the improved TOPSIS method is used for ranking six aircraft: A320, A330, A340, B737, B747, B767 based on cabin environmental efficiency and safety. [ABSTRACT FROM AUTHOR]

This paper studies how human activity affects energy consumption and how such activity can be leveraged from electricity meter data. Using 15-minute interval data and applying the methods introduced in this paper, it is possible to identify in which days there was human activity and extract daily routines from such data. This type of insight into the way people consume energy helps understanding energy use at a deeper level, and realise where significant energy savings can be made. [ABSTRACT FROM AUTHOR]

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

• Globally, population weighting substantially changes degree-days rate of change. • Choice of base temperature strongly influences degree-day calculation. • For typical base temperatures, heating degree-days are falling at twice the rate that cooling degree days are increasing. • Within countries, heterogeneity in population changes determines relative changes in heating and cooling degree-days. Anthropogenic greenhouse gas emissions are driving global increases in temperature. This rise will likely lead to an increase in demand for cooling in the coming years. However, increasing temperatures are not the main explanatory factor for why the world is moving towards more cooling. This paper compares population and area-weighted cooling and heating degree-days derived using ERA5-Land reanalysis temperature, to show that population growth in warmer parts of the world drives cooling demand globally. The analysis shows that mean global area-weighted heating degree-days have fallen 8.46 °C days/year, whereas population-weighted heating degree-days have fallen by 12.5 °C days/year. At the same time, mean global area-weighted cooling degree-days have risen by 3.0 °C days/year, while population-weighted cooling degree-days have risen at 6.0 °C days/year. By using sub-country analysis, this paper shows that population-weighted degree-days can substantially differ from area-weighted degree-days. Finally, the findings highlight that the choice of heating and cooling degree-day base temperature is the most important parameter in the variability of degree-days and will need to be understood better in order to accurately account for future heating and cooling energy demand. [ABSTRACT FROM AUTHOR]

Energy consumption of lift systems is one key issue in modern high-rise buildings, drawing property owners' attention and accounting for 6% of total electricity consumption by end-use in Hong Kong. A Benchmarking Energy Performance Indicator (BEPI), , measured in J.kg−1.m−1 and proposed by the first author of this paper for lift installations is mentioned in the Technical Guidelines of the Building Energy Code , as one emerging good engineering practice. BEPI is a fair parameter for comparing energy performance of lifts irrespective of their brand, model, travel, speed, capacity and control. It considers energy performance due to both the motor drive efficiency and traffic control intelligence. However, one main obstacle to make it statutory is the lacking of a generic monitor able to retrieve three sets of real time data (normally unavailable on an existing lift) to compute the BEPI, namely energy consumption, instantaneous car load and traveling distance, and to collect ample data statistically reliable enough to determine an acceptable threshold value. This paper describes the development of a proof-of-concept prototype BEPI monitor by giving full technical details on the rationale and history of development, the hardware components, software framework, and updated numerical results of one implementation inside the headquarters of Electrical & Mechanical Services Department (EMSD) of HKSAR. Experiences in installing and calibrating those external sensors that make the whole system non-disturbing to normal lift operation, as well as the procedures in computing the BEPI, are discussed in greater details. It has been shown that the BEPI is traffic dependent but the weekly average of it is relatively stable, confirming it may be robust enough for benchmarking purpose. The solution described in this paper may help roll out the monitoring of BEPI for all lifts inside and outside Hong Kong. [ABSTRACT FROM AUTHOR]

[Display omitted] • Unique preconditions, specific characteristics for use can be boundary conditions. • Energy use, users' behavior, lighting are correlated conditions within buildings. • Boundary conditions can help explain the occupants' behaviour and the use of spaces. • Knowledge of boundary conditions helps to understand how to design buildings. • Boundary conditions can be building/ user/ input-values and method-specific. This paper aims to review the boundary conditions (B/C) in specific categories (energy, building use, and lighting) within non-residential buildings to pave the way to a better understanding of users' requirements and needs of the built environment. For this paper, B/C are understood as unique preconditions, specific characteristics for use, determining specific features of buildings, enabling an accurate understanding of non-residential spaces concerning energy use, user behaviour, and lighting. This paper describes the results of an overall quantitative (1st method) review and a systematic review (2nd method) of boundary conditions and their factors within different types of non-residential buildings from the users' perception. Followed by a qualitative experts' literature review (3rd method) on B/C within offices, schools, and hospitals chosen by a team of international experts working together on Subtask A: User perspective and requirements, Task 61 IEA (International Energy Agency): Solutions for daylighting and electric lighting. The first review method led to the selection of 21 papers. The second method resulted in the selection of 7 papers out of 93,143 found in Scopus; during the 3rd review, experts collectively chose 74 additional papers focussing on the users' factors contributing to specific B/C. The scope of this paper is limited only to offices, schools, and hospitals. Based on the findings, the authors recognise a broad definition of boundary conditions from specific values, and conditions to interconnected factors, user profiles, functions of the building types, and operating hours. This paper is an overview of B/C factors found in the literature that can help explain the occupants' behaviour and the use of spaces. B/C are often type of building/user/location/situation/simulation input-values and method-specific. Therefore, they cannot be widely applicable but offer patterns and help to understand the correlations between various factors shaping the built environment. A better comprehension of the reasons for identifying B/C and their factors can help in developing a deeper knowledge of how we use buildings to find optimal ways to design them. [ABSTRACT FROM AUTHOR]

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

This paper illustrates an energy upgrading proposal of a historical building in Naples (Italy): Palazzo Fuga, better known as the Real Albergo dei Poveri which was built in the second half of the XVIII century. The proposal consists of energy efficiency interventions on the building envelope and its plants. As energetic and thermal aspects are concerned, simulations were performed in order to size a photovoltaic system, considering lighting aspects, and to evaluate the improvement of the building energy performance. Furthermore, thermal and lighting measurements were performed with a double aim: to verify the factual state of the building and to calibrate the calculation tools to the particular situation. In this paper obtained results concerning the feasibility of interventions will be presented and discussed. [ABSTRACT FROM AUTHOR]

This paper recognizes the synergetic relationship and inextricable linkage between conservation and sustainability. Heritage preservation maximizes the use of existing materials and infrastructure, reduces waste, and preserves the historic character of older towns and cities. This paper also presents technical advances in BPS/BES (Building Performance Simulation/Building Energy Simulation), and in co-simulation for energy efficiency optimization, RES integrated historic building's greening and historic neighborhoods, settlements, as well as whole cities urban re-planning. Further, the paper presents initial results of the Aviation Museum in Belgrade preliminary energy refurbishment study focusing results obtained via BPS and co-simulation. Foreseen is the deep energy refurbishment aimed to reach the “total building performance optimization” encompassing multifunctional optimization of building's skin – glazing replacement with the semi-transparent PV glazing and day-lighting control with reference to the artifact preservation, as well as the IEQ (Indoor Environment Quality) control analysis. This study shows that the RES integrated refurbishment can result in the Museum's Zero CO 2 emission and even in the sustainable Energy + status (producing more than using electricity). Finally, needs for most current ICT knowledge use and refurbishment technologies development, as well as integrated design, including particularly relevant benchmarking and rating system development are outlined. [ABSTRACT FROM AUTHOR]

This paper introduces a temperature-based approach – called the Days Exceeding Threshold-Toa (DET-Toa) method to detect persisting small increase or decrease in the normal building energy consumption. This method identifies an abnormal energy consumption fault when the deviation between the measured and simulated consumption is greater than one standard deviation of the residuals in the baseline period and persists for at least 20 days which are consecutive when ordered according to increasing or decreasing outside air temperature. The fault detection capability of the method is evaluated with simulation tests with two on-campus buildings. Ten synthetic control changes were assumed to happen and lasted for one year for each building. In the test, the DET-Toa method showed superior capacity for detecting abnormal building energy consumption compared with the DET-Date method. It successfully detected 19 synthetic control changes in the 20 simulation test cases. The reason for the better performance of the DET-Toa method is discussed and demonstrated in an example case. The smallest faults that could be identified by the DET-Toa method in the two analyzed buildings and their related energy consumption impact statistics are also provided in the paper. [ABSTRACT FROM AUTHOR]

In this paper a model for choosing the proper modular configuration of a point-focus concentrating thermo-photovoltaic (CPV/T) system according to the domestic user energy demands, is presented. The main aim is to evaluate several modular configuration solutions in terms of energy and economic performances and space occupied. First of all, the main CPV/T system components are chosen: optics, solar cells and tracking system. A point-focus system with parabolic mirrors, triple-junction cells and dual-axis tracking system is considered. The choice of the proper CPV/T system configuration is realized considering an equations system that defines the electric and thermal performances in terms of solar radiation, cells number and concentration factor. The choice model presented in this paper allows to configure, referring to different Italian zones, the CPV/T system under several working conditions minimizing spaces and costs. It is observed that CPV/T modules with a lower cells number allow high flexibility to satisfy the loads. The high concentration level determines interesting solutions related to a domestic application from the energy and economic point of view, above all in southern Italy, during the CPV/T system life cycle. Finally, CPV/T systems allow a significant reduction of CO 2 emissions due to the primary energy savings. [ABSTRACT FROM AUTHOR]

This paper summarises the results of a study to identify and quantify the potential impact of measures and policies to reduce the energy consumption of air conditioning in European countries over a 10-year period. The market penetration of air conditioning in Europe has increased steadily over several decades. On a “business as usual” basis, its energy consumption would increase by over 50% by 2020. Measures to restrict this increase are therefore important, but the savings potential straddles several overlapping areas which are subject to different constraints including those imposed by the replacement rates of systems and products, and the refurbishment rates of existing buildings. This work reported in this paper is based on the use of information from a variety of sources in a highly disaggregated model to assess the “realisable” savings over a 10-year period, taking into account relevant constraints. The paper summarises results at a European level, focussing on those cases that generate the greatest realisable savings. Savings are possible in three areas: improved equipment and system efficiency, reduced cooling loads, and more effective operation. The cases that offer the largest savings provide the basis for recommendations for policy measures, often using existing policy instruments. In addition, areas where further work is needed are identified. More detailed results and information about the study and additional recommendations can be found at http://www.bre.co.uk/searchresults.jsp?category=5&q=energy+management [1] . [ABSTRACT FROM AUTHOR]

Growing demand for urban built spaces has resulted in unprecedented exponential rise in production and consumption of building materials in construction. Production of materials requires significant energy and contributes to pollution and green house gas (GHG) emissions. Efforts aimed at reducing energy consumption and pollution involved with the production of materials fundamentally requires their quantification. Embodied energy (EE) of building materials comprises the total energy expenditure involved in the material production including all upstream processes such as raw material extraction and transportation. The current paper deals with EE of a few common building materials consumed in bulk in Indian construction industry. These values have been assessed based on actual industrial survey data. Current studies on EE of building materials lack agreement primarily with regard to method of assessment and energy supply assumptions (whether expressed in terms of end use energy or primary energy). The current paper examines the suitability of two basic methods; process analysis and input–output method and identifies process analysis as appropriate for EE assessment in the Indian context. A comparison of EE values of building materials in terms of the two energy supply assumptions has also been carried out to investigate the associated discrepancy. The results revealed significant difference in EE of materials whose production involves significant electrical energy expenditure relative to thermal energy use. [ABSTRACT FROM AUTHOR]

This paper considers energy conservation practices in Qatar with special emphasis on commercial buildings. Energy conservation approaches are classified into five main areas and an Energy Conservation Matrix (ECM) database for buildings is developed. The ECM maps the energy conservation techniques/technologies to models versus their application domain. Three scenarios (building envelope design, change in customer behavior, and consideration of renewable energy supply) are analyzed to study different alternatives for efficiency improvement. The analysis is done in a case hotel. The analysis shows that, the energy conservation potential of using envelope redesign for the case study is about 7.5% while conservation through behavior change ranges between 2.74% and 15.80%. The conserved energy potential ranges between 10% and 24.12% of the site energy in the combinatorial scenario that integrated envelope design alternatives with customer behavior change. The renewable energy (RE) scenario conserves energy, indirectly, by using green energies generated from renewable sources. The output shows that due to changes as per the three scenarios, total CO 2 emissions of the building are also reduced. The analysis shows that adoption of 30% RE alternative can reduce emissions by about 27% with respect to the reference scenario. It is believed that the scenarios developed in this paper and the results obtained will motivate the designers to consider alternative designs or redesigns in the large scale commercial buildings. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model is formulated to quantify the social impact an individual has on his/her community when he/she performs any energy efficiency project and transmits that information to his/her neighbours. This model is called the expected power savings model; it combines direct and indirect expected power savings of the energy efficiency project for each individual within the network. The indirect savings are quantified through the social interactions people in the network. The example used in this paper illustrates the effectiveness of the model by identifying the households who should have free solar water heaters installed in their residential houses based on their influence through interactions in their community. Two case studies are considered in this paper, single and multiple sources case studies. In the multiple source case study, the results show that it is not necessarily the people with the highest connections who provide the maximum expected power savings. [ABSTRACT FROM AUTHOR]

• Operating time of SMEs is estimated using a SOM and hourly demand data. • The estimated operating time is used for hourly AC demand disaggregation. • The average errors of yearly AC demand estimation are better than 10%. • Hourly disaggregation highlights season and time period of AC demand peaks. Despite the growing penetration of smart meters and energy management systems in recent years, few tools have been proposed for energy demand data analysis and visualization to promote energy conservation in small- and medium-sized enterprises (SMEs). In this paper, we propose a new method consisting of operating time estimation and simplified disaggregation based only on the SME's hourly electricity demand data and actual outdoor temperature data. The method first estimates the operating time using a self-organizing map, which categorizes the hourly data as operating, half-operating, or non-operating time and eliminates manual parameter adjustment that requires user trial and error. Then, using the estimated operating time, our simplified disaggregation method estimates hourly air conditioning (AC) demand considering the variation in each office's temperature at which air conditioners are switched on. The method was evaluated using a public business energy management system dataset from Japan, and the average errors of yearly AC demand estimation were better than 10%. The outputs of the proposed method may provide information that is more relevant, reliable, and specific to each customer and efficiently promote energy conservation measures in SMEs. [ABSTRACT FROM AUTHOR]

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

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

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

In HVAC systems, the application of fault detection and diagnosis (FDD) for chillers has brought many benefits to building utilities, i.e., indoor thermal comfort, energy saving, and energy consumption management. Although recent studies have achieved great progress on chiller fault diagnostic problem, some serious issues have been raised in these studies and need to be resolved comprehensively. In that context, two problems are listed and studied in this paper namely outliers detection and the insufficiency of labeled data. To deal with outliers mixed in chiller data, this paper proposes a supervised multiclass deep autoencoding Gaussian mixture model (S-DAGMM) algorithm which is an ensemble model of individual unsupervised DAGMMs. This mechanism helps S-DAGMM to detect ambiguous outliers in both training and testing data. In addition, this paper proposes to use DAGMM to pretrain a deep neural network (DNN). Since DAGMM can learn the data distribution from the unlabeled data, it can prevent DNN from getting stuck on local optima. Comprehensive experimental results in this study have proved the outstanding performance of the proposed approach, compared with state-of-the-art rival methods. [ABSTRACT FROM AUTHOR]

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

The COVID-19 pandemic has significantly affected people's behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems. [ABSTRACT FROM AUTHOR]