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Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain. [ABSTRACT FROM AUTHOR]

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [ABSTRACT FROM AUTHOR]

Energy efficiency in buildings has a central role to play in reaching the climate neutrality goal set by the European Green Deal. With detailed knowledge of their building stock and the profile of their occupants, regional and local authorities can forge an important link between financiers, industry professionals and homeowners to ensure the successful deployment of financial schemes that support the uptake of energy efficiency upgrades. This paper provides a first assessment of the role of regional and local authorities by reviewing relevant initiatives and programmes on energy efficiency. Based on a questionnaire completed for more than 150 schemes, it explores how European municipalities and regions stimulate energy upgrades in residential, commercial and public buildings through public financial support. It is found that 60% of the examined schemes are offered in the form of grants and subsidies, and 45% of them targeted residential upgrades. The use of EU cohesion policy funds in supporting regional schemes, and promotion of energy efficiency investments at local level through the European Covenant of Mayors initiative are also explored. In spite of possible resource limitations at this level of governance, regional and local authorities are in a good position to utilise European structural or research funds to develop financial schemes, as standalone programmes or blended with national ones, thus providing additional support and funds for deep renovations. The results suggest that energy efficiency in buildings has become an important part of local and regional strategies in several European countries, but could be further enhanced. [ABSTRACT FROM AUTHOR]

Non-residential buildings are responsible for more than a third of global energy consumption. Estimating building energy consumption is the first step towards identifying inefficiencies and optimizing energy management policies. This paper presents a study of Deep Learning techniques for time series analysis applied to building energy prediction with real environments. We collected multisource sensor data from an actual office building under normal operating conditions, pre-processed them, and performed a comprehensive evaluation of the accuracy of feed-forward and recurrent neural networks to predict energy consumption. The results show that memory-based architectures (LSTMs) perform better than stateless ones (MLPs) even without data aggregation (CNNs), although the lack of ample usable data in this type of problem avoids making the most of recent techniques such as sequence-to-sequence (Seq2Seq). [ABSTRACT FROM AUTHOR]

Over the last few decades, energy efficiency has received increasing attention from the Architecture, Engineering, Construction and Operation (AECO) industry. Digital Twins have the potential to advance the Operation and Maintenance (O&M) phase in different application fields. With the increasing industry interest, there is a need to review the current status of research developments in Digital Twins for building energy efficiency. This paper aims to provide a comprehensive review of the applications of digital twins for building energy efficiency, analyze research trends and identify research gaps and potential future research directions. In this review, Sustainability and Energy and Buildings are among the most frequently cited sources of publications. Literature reviewed was classified into four different topics: topic 1. Optimization design; topic 2. Occupants' comfort; topic 3. Building operation and maintenance; and topic 4. Energy consumption simulation. [ABSTRACT FROM AUTHOR]

This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential and commercial areas to calculate the total PV production for each type of load to achieve a balanced energy area, considering (i) the number of buildings, (ii) the type of load, (iii) the peak load, and (iv) the total PV array area in m2 per building. The results of the modelling study using real data demonstrate that the anticipated total PV production in residential and commercial areas is sufficient to meet local peak demand, and there is an excess of power that can either be stored locally or exported to the grid. [ABSTRACT FROM AUTHOR]

The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building's energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results. [ABSTRACT FROM AUTHOR]

The incontestable evidence of climate change and extreme weather events has brought the entire world to the brink of a red code alert, which determined the need to establish targets for limiting greenhouse gas emissions and the other actions that can be taken to solve them. The paper presents aspects of limiting the contribution of buildings to climate change through energy efficiency measures with an emphasis on the importance of the nZEB concept. The need to reduce the consumption of energy from fossil sources and increase the contribution of energy renewable sources is discussed, in a realistic context. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive exploration of energy efficiency dynamics in non-residential public buildings such as schools, swimming pools, hospitals, and museums. Recognizing the distinct energy consumption patterns of each building type, the study accentuates the unique challenges they present, with a particular focus on the continuous and intensive energy demands of hospitals and the unparalleled energy needs of swimming pools. Through an extensive review of various case studies, the research unveils prevailing energy consumption trends, highlighting the role of metrics in assessing energy efficiency and the inherent challenges these metrics face in ensuring uniformity and direct comparability. A core element of this analysis emphasizes the dual nature of technical retrofitting, categorizing interventions into passive and active measures. The research delves into the sustainability imperatives of energy interventions, exploring the economic motivations underpinning retrofit decisions, and the intricate relationship between advanced technological solutions and the behavioral tendencies of building operators and users. Additionally, the study uncovers the influence of external determinants such as climatic factors and government policies in shaping energy consumption in public buildings. In synthesizing these findings, the paper offers insightful recommendations, emphasizing the need for an integrated approach that harmonizes technological innovations with informed operational habits, aiming to optimize energy efficiency in public non-residential buildings. [ABSTRACT FROM AUTHOR]

The rise in living standard and the rapid development of the economy have led to an increase in the daily production of domestic wastewater. Environmental policies aim to reduce pollution, and appropriate technologies in the field of wastewater heat recovery could contribute to this goal. Technologies such are heat pumps and heat exchangers enable wastewater heat recovery and contribute to reduction of wastewater thermal pollution at the source. Also, using the recovered heat can meet some of the energy needs of buildings and reduce CO2 emissions. This paper reviews the literature on wastewater heat recovery in buildings and the opportunities to reduce energy consumption and greenhouse gas emissions through the use of wastewater heat recovery technologies. Also, the paper presents the methodology for evaluating the effects of the potential of wastewater heat recovery in buildings, and gives an analysis of the key factors affecting the wastewater heat recovery potential. Given the constant potential of heat recovery and its widespread availability at the source, this study concludes that future strategies for creating more sustainable societies will rely more heavily on heat recovery from wastewater. [ABSTRACT FROM AUTHOR]

This paper reviews applied single and hybrid solar energy-saving techniques with emphasis on solar chimney, Trombe wall, and photovoltaics for building energy consumption and thermal comfort. Solar energy techniques can be broadly classified into passive and active. Passive strategies refer to collecting, storing, and distributing solar energy without relying on electrical or mechanical components. In contrast, active techniques rely on mechanical and electrical components. The discussed passive devices include solar chimney and Trombe wall. Different design configurations have been reported to influence various buildings' thermal and electrical performances. On the other hand, active solar strategies in buildings including photovoltaic technologies, are discussed. Moreover, applied the hybrid system of these techniques in building is discussed. The review paper critically analyzes these systems' energy and thermal performances to demonstrate their significance in building applications. The significant findings of these studies demonstrate the capability of these systems to provide thermal comfort and considerable energy savings. The review studies indicated that passive architecture could improve thermal (as high as 20%) and visual comfort and reduce electricity consumption by 46%. Besides, several highlights on BIPV and BIPV/T systems are discussed, showcasing their status, recent research, development, and applications toward achieving building energy efficiency. Throughout the review, it is seen that solar technologies applied in buildings have undergone a remarkable revolution, demonstrated significant energy-saving potentials and opening the road for application in net-zero energy buildings. Further research is required on some of the technologies to assess the techno-economic feasibility of these systems in the building sector. [ABSTRACT FROM AUTHOR]

Environmental, economic, and social activities involve inherent spatial dimensions. The geospatial information system (GIS), a platform containing principles, methods, and tools to link, create, visualize, analyze, and model artificial activities and environment, provides the possibility to develop sustainability in the building sector. With globally political collaborations across governments, the demands to manage and visualize sustainable data (e.g., building energy and environment with geospatial reference) and implement more rigorous building modelling are increasing. A systematic mapping at multiple scales will help urban engineers, architectural engineers, policymakers, and energy planners identify emission hotspots, locate spatial resources, restructure district energy mix, and achieve net zero energy targets. To achieve net zero energy goals, it is crucial to minimize energy consumption, improve energy efficiency, and most importantly, apply renewable energy in buildings. However, these processes imply many aspects and challenges, regarding e.g., data availability, scalability, integrability, and a lack of clear and applicable frameworks. In this conceptional perspective paper, we aim to explore the potential of applying and installing renewable energy in net zero energy buildings using the GIS. More specifically, the described virtual framework will effectively support policy- and decision-makers in optimizing the energy structure, reducing building emissions, and applying renewable energy technologies. We also present challenges, limitations, and future directions for real practice. [ABSTRACT FROM AUTHOR]

The paper provides an architectural analysis of the switchable PV-EC glazing technology based on combining photovoltaic (PV) technology with electrochromic (EC) glazing. The integration of these technologies is considered to constitute futureoriented façade solutions in shaping buildings that are energy-saving and environmentally friendly. The paper aims to define theoretical models of windows using PV-EC technology as solutions adequate from the architectural point of view. To achieve this goal, a comparative analysis of three PV-EC technologies was conducted, i.e., side-by-side (SBS) technology and tandem technologies, namely tandem solid technology (TST) and tandem liquid technology (TLT). The analysis covered functional aspects related to such issues as thermal and visual comfort, energy and aesthetics. The analysis led to extracting the features of the three compared technologies; consequently, their strengths and weaknesses were determined. As a result, seven window models were developed which, based on the above analysis and the insights derived from it, were recognized as the solutions in which the potential of PV and EC technology is best used. The dominant advantages of SBS, being the most developed technology and one with the greatest flexibility in construction applications, are indicated. The research is of a contributory nature, as it constitutes the basis for further numerical and simulation research. Such studies may prove useful to architects in making design decisions, especially at the initial design stages. However, at the current stage of technological development, the study mainly serves as an introduction to further research on improving the PV-EC properties towards integration with the building and its architecture. [ABSTRACT FROM AUTHOR]

Data on the number of floors is required for several applications, for instance, energy demand estimation, population estimation, and flood response plans. Despite this, open data on the number of floors is very rare, even when a 3D city model is available. In practice, it is most often inferred with a geometric method: elevation data is used to estimate the height of a building, which is divided by an assumed storey height and rounded. However, as we demonstrate in this paper with a large dataset of residential buildings, this method is unreliable: <70% of the buildings have a correct estimate. We demonstrate that other attributes and characteristics of buildings can help us better predict the number of floors. We propose several indicators (e.g. construction year, cadastral attributes, building geometry, and neighbourhood census data), and we present a predictive model that was trained with 172,000 buildings in the Netherlands. Our model achieves an accuracy of 94.5% for residential buildings with five floors or less, which is an improvement of about 25% over the geometric approach. Above five floors, our model has only a slight improvement on the geometric approach (5%). The main culprit is the lack of training data for tall buildings, which is uncommon in the Netherlands. [ABSTRACT FROM AUTHOR]

Wood is one of the most abundant biomaterials on Earth, which has been used for centuries in construction applications including furniture, roofing, flooring, and cabinetry. However, wood chips—which are a low-quality and plentiful waste byproduct of lumber milling, woodworking, and shipping operations—have low economic value and complicated disposal methods. In this paper, we propose a strategy for wood chip reuse through the fabrication of bio-based building insulation foam. Through a high-temperature chemical treatment delignification process, we introduced additional small pores within the wood chips, effectively lowering their thermal conductivity, and used them in combination with a binding agent to produce a porous insulation foam. The porous insulation foam achieved a low thermal conductivity of 0.038 W/(m·K) and a high compressive strength of 1.1 MPa (70% strain). These characteristics demonstrate that wood waste can be repurposed into an effective building material, addressing challenges in both waste management and sustainable construction. [ABSTRACT FROM AUTHOR]

Development of new building HVAC control algorithms has grown due to needs for energy efficiency and operational flexibility. However, case studies demonstrating new algorithms are largely individualized, making algorithm performance difficult to compare directly. In addition, the effort and expertise required to implement case studies in real or simulated buildings limits rapid prototyping potential. Therefore, this paper presents the Building Optimization Testing Framework (BOPTEST) and associated software for simulation-based benchmarking of building HVAC control algorithms. A containerized run-time environment (RTE) enables rapid, repeatable deployment of common building emulators representing different system types. Emulators use Modelica to represent realistic physical dynamics, embed baseline control, and enable overwriting supervisory and local-loop control signals. Finally, a common set of key performance indicators are calculated within the RTE and reported to the user. This paper details the design and implementation of software and demonstrates its usage to benchmark a Model Predictive Control strategy. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Architecture, Engineering and Construction (AEC) industry has been utilizing Decision Support Systems (DSSs) for a long time to support energy efficiency improvements in the different phases of a building's life cycle. In this context, there has been a need for a proper means of exchanging and managing of different kinds of data (e.g., geospatial data, sensor data, 2D/3D models data, material data, schedules, regulatory, financial data) by different kinds of stakeholders and end users, i.e., planners, architects, engineers, property owners and managers. DSSs are used to support various processes inherent in the various building life cycle phases including planning, design, construction, operation and maintenance, retrofitting and demolishing. Such tools are in some cases based on established technologies such Building Information Models, Big Data analysis and other more advanced approaches, including Internet of Things applications and semantic web technologies. In this framework, semantic web technologies form the basis of a new technological paradigm, known as the knowledge graphs (KG), which is a powerful technique concerning the structured semantic representation of the elements of a building and their relationships, offering significant benefits for data exploitation in creating new knowledge. In this paper, a review of the main ontologies and applications that support the development of DSSs and decision making in the different phases of a building's life cycle is conducted. Our aim is to present a thorough analysis of the state of the art and advancements in the field, to explore key constituents and methodologies, to highlight critical aspects and characteristics, to elaborate on critical thinking and considerations, and to evaluate potential impact of KG applications towards the decision-making processes associated with the energy transition in the built environment. [ABSTRACT FROM AUTHOR]

Statistics confirm that in the EU, buildings consume approx. 40% of final energy, which accounts for 36% of total European greenhouse gas production after energy consumption. Energy efficiency measures are urgently needed to effectively reduce greenhouse gas emissions. Decreasing fossil fuel consumption would have a major impact on declining energy demand per unit of gross domestic product. In 2014, European leaders adopted the ambitious Climate and Energy Framework 2030, in which the three key targets were raised: a 40% reduction in greenhouse gas emissions by 2030, a 27% reduction in RES and a 27% increase in energy efficiency. Then, in November 2016, the European Commission (EC) proposed the Clean Energy for All Europeans package, designed to support and accelerates the transition to a carbon-free energy system. This paper comprises a case study of the current situation of these matters in Romania and focuses on steps and methodology of what actions are reliable and available to be put into practice on this matter. [ABSTRACT FROM AUTHOR]

Building characteristics, household compositions, lifestyles, and home equipment are recognized as the main factors influencing residential energy consumption, which has been a subject of extensive exploration for many years now. However, the quantitative correlation models between the above factors and residential end-use energy have not been fully studied. This paper aims to explore the determinants of residential end-use energy consumption by a comprehensive analysis based on the factors of building characteristics, household compositions, lifestyles, and home equipment. For this purpose, we investigated and collected the building information of 66 households and obtained the data through an installed measurement system of the annual residential end-use energy from July 2019 to June 2020. Subsequently, six multiple regression models were used to quantitatively analyze the valid determinants of each end-use energy. The main results were as follows: for cooling energy consumption, the greatest effective variable was FM_no (22–59, number of family members aged 22 to 59); the most influential variable was found to be FM_no (number of family members) for DHW and appliances energy consumption; for lighting and cooking energy consumption, the most effective variables were AREA (floor area) and Cooking (average daily cooking hours), respectively. Moreover, the order of influence of building characteristics, household compositions, lifestyles, and home equipment over each end-use energy consumption is as follows: households > equipment > lifestyles for cooling and DHW, households > buildings > equipment for lighting, equipment > lifestyles for appliances and cooking. [ABSTRACT FROM AUTHOR]

The rational use of energy systems is one of the main discussions in sustainability in the 21st century. Water pumping systems are one of the most significant consumers of electricity in urban systems, whether for urban water supply, sewage, or use in vertical buildings. Thus, this work aims to present Industry 4.0 (I4.0) technologies applied in buildings' water pumping systems, focusing on energy efficiency, supervision, and control of the pumping system. The work involves four steps: (i) identifying the existing I4.0 technologies and (ii) mapping the possibilities of applying Industry 4.0 technologies in building pumping systems. The study includes the analysis of (16) articles published in journals between 2018 and June 2021 to identify I4.0 technologies cited in the publications. It identified and grouped eighteen (18) technologies based on twenty-two (22) terms observed in the papers. The study classified the identified technologies into three possible applications in a building water pumping system. The applications include: (i) directly applicable, (ii) partially applicable, and (iii) application not yet identified. Therefore, the study presents the advantages of I4.0 technologies developed primarily for the industry sector, also applicable in residential building water pumping systems. These technologies' benefits include energy efficiency, user control, a reduction from periods of failure of the pumping system (maintenance), water quality, and moving towards Intelligent Pumping or Pumping 4.0. [ABSTRACT FROM AUTHOR]

Copyright of European Policy Analysis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Among all the activities in a society, construction has a key role in environmental, social, and economic pillars. Construction is also responsible for a considerable amount of waste production, energy consumption, pollutant gas emissions, and consumption of nonrenewable natural resources. Regarding energy consumption, a high demand for building operational energy has been observed in the last decades due to the more demanding requirements of the users with a continuous search for better thermal comfort in their homes, namely in developed countries. In Portugal, for instance, more than 20% of the electricity consumed is related to residential buildings, which is based on CO2 emissions and other pollutants that negatively affect the environment. Much of this consumed energy is a result of the HVAC systems installed inside buildings to provide users with thermal comfort. One exciting opportunity to mitigate buildings' operational energy consumption while contributing to thermal user comfort is the use of passive solutions. Even though several passive options are available and constantly under research, their use is still considered limited. This paper overviews and highlights the potential of energy-efficiency passive strategies, namely for Mediterranean-climate countries, where passive solar technologies can be set as a viable solution, as this climate is mainly known for its solar availability (solar hours and solar irradiance). A comprehensive overview of innovative and traditional housing passive solutions currently available is presented and discusses the main advantages, disadvantages, and concerns contributing to the optimal use of climatic conditions and natural resources in those regions. [ABSTRACT FROM AUTHOR]

In the areas after natural disaster strikes, the ground communication network can be failed, due to the damage of the communication infrastructure. However, during or after the natural disasters such as earthquakes or tsunamis, ground vehicles may not enter the affected areas easily to set up mobile base stations. Unmanned Aerial Vehicles (UAVs) can be an alternate to provide emergency coverage for ground nodes (GNs). Therefore, how to determine the best location for UAV to achieve the maximum coverage is a key issue. In this paper, an adaptive UAV deployment scheme is proposed to solve the coverage problem of UAV- aided GNs communication. The objective is to optimize the location of the UAV to cover as many GNs as possible and reduce communication energy consumption. We construct a unique analysis method assisted by the collected ground information to solve this problem. First, we propose an information collection method based on the communication probability of Line-of-Sight (LoS) to guarantee the integrity of ground information acquisition. Then, based on the results of the information collection, a virtual obstacle model is built around each GN. Meanwhile, the UAV’s coverage problem is decomposed from the horizontal and vertical dimensions to simplify the difficulty of solving the problem. Finally, the best location of the UAV and the optimal transmission power of GNs can be obtained through the iterative methods and the power control, respectively. The extensive simulation results demonstrate that the proposed deployment scheme outperforms its counterparts. [ABSTRACT FROM AUTHOR]

Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes. [ABSTRACT FROM AUTHOR]

A model of the influence of the climatic conditions of Slovakia on the consumption of firewood and the production of emissions in creating thermal comfort in a heated building is presented in the paper. The temperature of atmospheric air ranges from -11°C to -18°C in winter and the number of days of the heating period in the range between 202-253 in individual localities of Slovakia is reflected in the size of heat losses of the heated building, fuel consumption, and emissions production. The presented model allows for balancing the consumption of firewood and emission production for individual localities in Slovakia, following the locality's climatic conditions, the heated object's size, the boiler's thermal efficiency, and the type of firewood. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption.

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

• Buildings potential for climate change mitigation is high. • Mitigation actions in the buildings sector can have significant social and economic values. • These enable the achievement of 4 SDGs, reinforce 7 other. • And are linked to the achievement of 5 SDGs (SDG3, SDG7, SDG8, SDG11, and SDG13). • Trade-offs were identified for 9 SDGs. Buildings are responsible for a big part of total GHG emissions; therefore, their climate change mitigation potential is high. But research shows that mitigation actions in the sector can have significant social and economic values beyond reduction of energy consumption and the associated GHG emissions. This paper summarizes these multiple impacts of mitigation actions in buildings, which are related to microeconomic and macroeconomic effects, health impacts, environmental benefits, better management of natural resources, impacts on social well-being, and energy security implications, and assesses their interactions (synergies and trade-offs) with the UN SDGs. The findings of our analysis show that climate change mitigation actions in buildings enable the achievement of 4 SDGs, reinforce 7 others, and are inextricably linked to the achievement of 5 SDGs, namely SDG3: good health and well-being, SDG7: affordable and clean energy, SDG8: decent work and economic growth, SDG11: sustainable cities and communities, and SDG13: climate action. On the other hand, trade-offs were identified for 9 SDGs, with rather minor implications, which can be further eliminated with the implementation of appropriate policies. [ABSTRACT FROM AUTHOR]

In areas with a deep floor plan, the distribution of natural light is not uniform. Consequently, relying solely on daylight may not suffice to meet the space's lighting requirements, necessitating the use of artificial lighting in darker areas. Therefore, a lighting system is needed that not only controls the glare near the windows but also increases the light at the end of the room and provides uniform daylight. One of the widely used systems is the "light shelf", which has three main functions: shading, increasing the depth of light penetration, and reducing glare. Review articles about light shelves were published in 2015 and 2017, while more than 80% of the studies have been carried out since 2016, and light shelves with more diverse forms and dynamic elements and many consolidations have been proposed. Therefore, there is a need for a more comprehensive review. The main question of this research is how different parameters (including climate, material, ceiling, and integrated systems) can help to increase the efficiency of light shelves. By using a systematic review, studies in the past three decades were classified in order to determine the effect of these parameters on improving lighting performance and controlling solar heat gain. [ABSTRACT FROM AUTHOR]

Thermal comfort is a complex issue in the built environment due to the physiological and psychological differences of each individual in a building. There is a growing worry over the environmental implications of energy use as a result of the warming of the global climate and the growth in the number of instances of extreme weather events. Many review articles have been written, but these reviews have focused on a specific aspect of occupant behavior and thermal comfort. To research the trends of thermal comfort and energy, this research adopted mixed reviews, i.e., quantitative and qualitative, to understand the state-of-the-art factors affecting the thermal comfort of occupants concerning energy, different occupant modeling approaches, functions, and limitations. The in-depth qualitative discussion provides deeper insights into the impacts of occupant behaviors, factors affecting thermal comfort, and occupant behavior modeling approaches. This study classified occupant behaviors into five categories: occupant characteristics, perceptions of the occupant, realistic behaviors, heat gain, and occupant interactions with the system. It also went further to classify the factors affecting the thermal comfort of users based on past works of literature. These include structural, environmental, and human factors. It was concluded that factors that have the most significant impact on energy are human, structural, and environmental factors, respectively. In addition, most of the occupant behavior modeling approaches that have been used in past studies have pros and cons and cannot accurately predict human behaviors because they are stochastic. Future research should be conducted on thermal comfort for different building functions by examining the varied activity intensity levels of users, especially in educational or commercial buildings. Additionally, a proper investigation should be carried out on how thermal insulation of structural members influences thermal comfort. These should be compared in two similar buildings to understand occupant behavioral actions and energy consumption. [ABSTRACT FROM AUTHOR]

Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodology in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention. [ABSTRACT FROM AUTHOR]

It is crucial to consider structural design issues in Trombe wall (T-wall) buildings to promote more suitable indoor climates and thermal comfort standards. Therefore, the present study examined the impact of two different T-wall designs and six different roof types on the energy and operational efficiency of a building located in a low-temperature and high-humidity winter climate. Ansys-CFX 15.0 software was employed to simulate the thermal and fluid dynamics behavior of the T-wall system, and flow, thermal comfort, energy, and exergy analyses were conducted. Three-dimensional simulation results and the pertinent literature data showed a good level of agreement, and the accuracy of the model was ensured. Outcomes revealed an average air velocity variation of 0.186 m/s and maximum average indoor air temperature variation of 3.3 °C between the six roof geometries. The highest air speed (0.988 m/s) was recorded for the gambrel roof while the lowest one (0.802 m/s) was recorded for the typical flat roof. The shed roof right with a rounded T-wall was more comfortable for standing and sitting activity than the others for the two T-wall shapes, and, at Y = 0.6 m and Y = 1.1 m, the average predicted percentages of dissatisfied (PPD) values were 31 and 28%, respectively. Furthermore, it was determined in the study that solar radiation intensity and T-wall and roof geometries had a significant effect on energy and exergy efficiency, and high energy and exergy efficiencies were achieved at higher solar intensity values. The best energy and exergy efficiencies were obtained for the butterfly and shed roof configurations. This study can serve as a reference for the thermal environment design of buildings with T-walls. [ABSTRACT FROM AUTHOR]

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This work focuses on a literature review that characterizes the most prominent lines of research on energy efficiency in educational buildings, including energy use intensity (EUI); the implementation of energy efficiency measurement; the results obtained by decreasing the EUI, energy consumption, and CO 2 emission; and the main relationships between energy consumption incidence variables. For these purposes, a systematic literature review is structured based on specialized databases, wherein the information is assessed using spreadsheets and visualization tools such as VOSviewer®. From the review, the authors were able to determine that the integration of energy efficiency with educational institutions is a growing line of research that offers opportunities for building an environmentally sustainable educational culture with high social impact. This paper discusses different modeling systems and policy assessment options that identifies complexity and dynamics constraints to explore new simulation methodologies, such as systems dynamics providing sustainable approaches within industry 4.0 based on the assessment of national energy efficiency policies through dynamic simulation models that allow significant savings in energy-consuming sectors. [ABSTRACT FROM AUTHOR]

Increased use of energy in buildings and HVAC systems requires advanced control schemes like model-based control to improve energy efficiency, which in turn requires accurate thermodynamic models of buildings. The Resistive-Capacitive (RC) method is a popular and versatile approach for thermal modeling of buildings. Despite this, it is not easy to find practical solutions of implementation of the RC method. It is the goal of this paper to clarify the RC method and demonstrate simple implementation of this method, especially for multi-zone buildings, which have more potential for energy savings from use of model-based control. This paper provides two contributions. First is a detailed explanation of the RC method, focusing on its use for developing a structure of a model and first-principles approach for estimation of parameters of a model. Second is a demonstration of an algorithm that enables automatic development of the structure of a model from basic information about a building (layout, construction elements) and its combination with data-based parameter estimation. Use of the algorithm is presented with a case-study on industrial multi-zone building, for which such a grey-box model is developed and analyzed. The resulting model is rapidly developed and used in a simulation with the measured data. The outputs of the model are compared with the measured temperatures and they show good fit. [ABSTRACT FROM AUTHOR]

The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a prominent solution to this challenge. However, the modern literature exhibits a plethora of RL methodologies, rendering the selection of the most suitable one a significant challenge. This work focuses on evaluating various RL methodologies for saving energy while maintaining adequate comfort levels in a residential setting. Five prominent RL algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Soft Actor-Critic (SAC)—are being thoroughly compared towards a baseline conventional control approach, exhibiting their potential to improve energy use while ensuring a comfortable living environment. The integrated comparison between the different RL methodologies emphasizes the subtle strengths and weaknesses of each algorithm, indicating that the best selection relies heavily on particular energy and comfort objectives. [ABSTRACT FROM AUTHOR]

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building's long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively. [ABSTRACT FROM AUTHOR]

An increasing number of federal and state regulations and financial incentive programs have been enacted in recent years to promote renewable energy systems integration in new buildings. This requires designers to analyze the initial and lifecycle costs of feasible renewable energy systems in order to optimize their integration in buildings during all design phases. The goal of this study is to support designers in generating optimal building schematic designs to maximize the integration and generation of onsite renewable energy. To accomplish this goal, this paper presents the development of a novel multi-objective optimization model that can be used during the schematic design phase to identify optimal building design decisions including dimensions, orientation, location on site, solid-to-void ratio, and the type and size of onsite renewable energy systems. The optimization objectives of the developed model focus on maximizing the generation of onsite renewable energy, maximizing savings-to-investment ratio of all integrated renewable energy, and minimizing both construction and renewable energy costs. An application example of optimizing the schematic design of a 6500 m2 building was analyzed to highlight the model original contributions and its novel capabilities. The results of this analysis highlight the original contributions of the developed model and its novel capabilities that can be used to maximize the generation of onsite renewable energy and its savings-to-investment ratio while minimizing building cost by optimizing building schematic design and renewable energy (RE) technology decisions. • Optimizing building schematic design with renewable energy integration. • Maximizing RE generation, savings-to-investment ration, and minimizing bilding costs. • Complying with building design requirements and renewable energy constraints. • Optimal building design parameters and renewable energy technologies. • Analyzing the impact of design decisions on renewable energy and building costs. [ABSTRACT FROM AUTHOR]

The buildings sector is the single most important end-user of final energy in the European Union and a significant contributor to carbon and greenhouse gas emissions. This work focuses on a review of available data that are used to calculate the annual emissions from electricity generation in the European Union and quantify indirect emissions from the use of electricity in the buildings sector. Historical data since 1990 are used to derive simple empirical correlations for the time evolution of emissions factors related to electricity production in each Member State of the European Union. More recent trajectories using data from the last ten years are also presented. The derived correlations can be easily integrated in building stock modeling and national studies to facilitate forward-looking projections of emissions from electricity use in buildings. The EU-27 averages 0.2883 kgCO2-eq/kWhel, ranging from 0.0456 kgCO2-eq/kWhel in Sweden up to 1.0595 kgCO2-eq/kWhel in Poland. As a case study, the derived coefficients are then used to quantify the indirect emissions from the electricity consumption attributed to the building stock in each EU Member State. The calculated total EU-27 GHG indirect emissions attributed to electricity consumption amounted to 215 MtCO2-eq for residential buildings and 201 MtCO2-eq for non-residential buildings. In addition, the proposed correlations are used to demonstrate how they can be used for more realistic future projections of emissions towards the national targets in Greece and Poland. [ABSTRACT FROM AUTHOR]

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

The healthcare industry significantly impacts the environment due to its high usage of energy and natural resources and the associated waste generation. This study applied a cradle-to-grave Life Cycle Sustainability Assessment (LCSA) approach to assess the environmental and social life cycles of public hospitals. One hundred twenty-four public hospitals were selected for the current study; their sustainability performance was compared with those certified by Leadership in Energy and Environmental Design (LEED). The comparison revealed several factors contributing to the poor sustainability performance of public hospitals. These include inadequate management, substandard planning, political interference, insufficient staffing and funding, high energy consumption, high expenses, inconsistent healthcare policies, and conventional building designs. System thinking was leveraged, and a causal loop diagram (CLD) was developed to visualize the interdependency of the identified indicators of LCSA. Based on the findings of the study, a policy framework is proposed to guide the development of sustainable healthcare buildings. The framework includes using eco-friendly materials and techniques in construction, harnessing solar energy, improving hospital management practices, promoting public awareness about sustainability, conserving energy and water, and adopting sustainable waste management and transportation. Additionally, it emphasizes addressing social issues such as improving indoor air quality, thermal comfort, lighting, acoustics, patient safety, and security and ensuring that healthcare services are accessible and affordable. This study contributes to the literature on sustainable healthcare buildings by providing a practical policy framework for achieving sustainability in the healthcare sector. [ABSTRACT FROM AUTHOR]

More than half of the world's population lives in cities. A large part of the emissions and energy consumption corresponds to buildings, both in the residential sector and in the service sector. This means that a large part of the measures taken by governments to reduce energy consumption and greenhouse gas emissions are focused on this sector. With this background, this paper studies energy consumption in city buildings and the CO2 emissions they produce. It only makes use of publicly available data. The analysis is made from the point of view of income per inhabitant, and the results are obtained per inhabitant and household. To facilitate the analysis of the results, an index has been defined. The main contributions of this work are to analyze energy consumption and emissions due to buildings, study them from the point of view of the income of their inhabitants, and consider cities individually. The proposed methodology has been applied to the case of Spain. A total of 145 Spanish cities that have more than 50,000 inhabitants have been studied. The results show that the higher the income, the higher the consumption and emissions. Electricity consumptions are almost inelastic, while those of thermal origin are greatly influenced by the level of income. Regarding CO2 emissions, the percentage of emissions of electrical origin with respect to total emissions is higher than that of thermal origin. In addition, the lower the income, the higher the percentage of emissions of electrical origin. [ABSTRACT FROM AUTHOR]

More than 50% of the world's population lives in cities. Its buildings consume more than a third of the energy and generate 40% of the emissions. This makes cities in general and their buildings in particular priority points of attention for policymakers and utilities. This paper uses population density as a variable to know its influence on energy consumption and emissions produced in buildings. Furthermore, to show its effect more clearly, the influence of the climate was eliminated. The usual energy consumption in buildings is thermal and electrical. The study was carried out at the city level, both per inhabitant and per household. The area actually occupied by the city was considered. The proposed method was applied to the case of Spanish cities with more than 50,000 inhabitants. The results show that the higher the population density, the higher the energy consumption per inhabitant and household in buildings. The consumption of thermal energy is elastic, while that of electrical energy is inelastic, varying more than 100% between extreme groups. Regarding CO2 emissions, the higher the population density, the higher the emissions. Emissions of electrical origin barely vary by 2% and are greater than those of thermal origin. In addition, the proportion of emissions of electrical origin, with respect to the total, decreases with increasing population density from 74% to 55%. This research aims to help policymakers and utilities to take the appropriate measures that favor the use of renewable energies and reduce CO2 emissions. [ABSTRACT FROM AUTHOR]

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities. [ABSTRACT FROM AUTHOR]

The built environment is the global sector with the greatest energy use and greenhouse gas emissions. As a result, building energy savings can make a major contribution to tackling the current energy and climate change crises. Fluid dynamics models have long supported the understanding and optimization of building energy systems and have been responsible for many important technological breakthroughs. As Covid-19 is continuing to spread around the world, fluid dynamics models are proving to be more essential than ever for exploring airborne transmission of the coronavirus indoors in order to develop energy-efficient and healthy ventilation actions against Covid-19 risks. The purpose of this paper is to review the most important and influential fluid dynamics models that have contributed to improving building energy efficiency. A detailed, yet understandable description of each model's background, physical setup, and equations is provided. The main ingredients, theoretical interpretations, assumptions, application ranges, and robustness of the models are discussed. Models are reviewed with comprehensive, although not exhaustive, publications in the literature. The review concludes by outlining open questions and future perspectives of simulation models in building energy research. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a multi agent control system (MACS), combined with a stochastic based approach for occupancy estimation. The control framework aims to maintain the comfort levels of a building in high levels and reduce the overall energy consumption. Three independent agents, each dedicated to the thermal comfort, the visual comfort, and the indoor air quality, are deployed. A stochastic model describing the CO2 concentration has been studied, focused on the occupancy estimation problem. A probabilistic approach, as well as an evolutionary algorithm, are used to provide insights on the stochastic model. Moreover, in order to induce uncertainty, parameters are treated in a fuzzy modelling framework and the results on the occupancy estimation are investigated. In the control framework, to cope with the continuous state-action space, the three agents utilise Fuzzy Q -learning. Simulation results highlight the precision of parameter and occupancy estimation, as well as the high capabilities of the control framework, when taking into account the occupancy state, as energy consumption is reduced by 55.9 % , while the overall comfort index is kept in high levels, with values close to one. [ABSTRACT FROM AUTHOR]