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Hybrid energy systems physically or conceptually combine various energy generation, storage, and/or conversion technologies to reduce costs and improve capability, value, efficiency, or environmental performance in comparison with independent alternatives. Hybridization is an interesting energy sector solution for plants to expand their flexibility, optimize revenues, and/or develop other useful products. Integrated hybrid energy systems' improved flexibility can hasten the integration of more renewable energy into the grid and help become closer to the target of zero-carbon energy grids. This paper aims to provide an updated literature review of design and applications of hybrid energy systems in buildings, focusing on economic, environmental, and technical viewpoints. This current study will analyze current and future trends toward hybrid energy systems for buildings and their functions in electrical energy networks as potential research study topics for the future. This study aims to enhance sustainable building techniques and the creation of effective electrical energy networks by offering insights into the design and applications of hybrid energy systems. The methodology used in this study entails assessing present and potential trends, as well as looking at hybrid energy system uses and designs in buildings. The higher flexibility of integrated hybrid systems, which enables enhanced grid integration of renewables, is one of the key discoveries. The discussion of potential research study themes and conceivable applications resulting from this research forms the paper's conclusion. [ABSTRACT FROM AUTHOR]

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted. [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]

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]

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]

As researchers and ultimately deployers of energy decarbonisation solutions, we collectively see significant but often siloed efforts that in isolation may appear as an appropriate solution to an aspect of energy decarbonisation. However, when systemwide thinking is applied, a former attractive solution may become more challenging and, likewise, a less attractive silo may become more appropriate as part of an energy systemwide approach. Thus, the aim of this paper is to combine proposed energy decarbonisation concepts, e.g., electrification, hydrogen, biogas etc., with the status of the system in which they intend to operate, and then highlight the barriers, opportunities, and alternatives that may come into play when the whole system is taken into account. This is a hypothetical study using the city of Belfast, Northern Ireland, UK as an example and reflects, in part, the city's desire to decarbonise while enhancing its economic prosperity. The "system" is defined as the region boundaries, i.e., Northern Ireland will supply the energy (all or in part) to the city of Belfast. The methodology deployed here therefore is a framework of energy thinking that is the basis of such energy decarbonisation plans at a city-wide level. [ABSTRACT FROM AUTHOR]

Sustainability rating systems for the built environment involve a multitude of indicators that are based on different types of data. This work capitalizes on an existing multicriteria assessment method and supporting decision-making tool at building and neighbourhood scale, to develop an enhanced method and tool at city scale. The main sustainability issues at building and city scales include site and infrastructure, energy and resources consumption, environmental loadings, climate change, environmental quality, water, waste, transportation, services, social aspects, economy and governance. Approximately 300 indicators distributed among the different scales are used to describe and quantify the various facets of sustainability. Specifically, the building scale includes a pool of 80 indicators of which 17 are key performance indicators (KPIs), the neighbourhood scale has 133 indicators of which 14 are KPIs and the city scale has a total of 99 indicators of which 10 are KPIs that were new additions to the existing method. The emphasis in this paper is given on elaborating the key performance indicators for cities and demonstrating their applicability through a case study. The common method and tools provide a flexible assessment system for local authorities and stakeholders to develop and assess sustainability plans. [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]

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]

Widespread adoption of residential heat pumps is predicted to create challenges for national and local electricity systems. Flexible operation of heat pumps could help smooth peak demand and better utilise renewables. Achieving these benefits involves many stakeholders from the heat pump and electricity sectors with different perspectives and expectations. This work brought together 52 experts from different parts of the UK system to discuss and debate the role of heat pump flexibility in a decarbonised electricity system in 2035. A co-production research model was adopted, designed to integrate diverse forms of knowledge and perspectives in the co-production of knowledge on heat pump flexibility. A series of participatory activities were undertaken including a one-day workshop. Elements of a common vision emerged, such as the anticipated widespread flexible operation of heat pumps as the cheapest way of running a heat pump and the likelihood of a highly automated and remote-controlled manner of operation. Disagreements and unknowns also emerged. This work aims to support stakeholders in planning for the social, technical and economic aspects of flexible heat pump operation in their own organisations. [ABSTRACT FROM AUTHOR]

To restrict global warming and relieve climate change, the world economy requires to decarbonize and reduce carbon dioxide (CO2) emissions to net-zero by mid-century. Carbon capture and storage (CCS), and carbon capture and utilization (CCU), by which CO2 emissions are captured from sources such as fossil power generation and combustion processes, and further either reused or stored, are recognized worldwide as key technologies for global warming mitigation. This paper provides a review of the latest published literature on small-scale carbon capture (CC) systems as applied in micro combined heat and power cogeneration systems for use in buildings. Previous studies have investigated a variety of small- or micro-scale combined heat and power configurations defined by their prime mover for CC integration. These include the micro gas turbine, the hybrid micro gas turbine and solid-state fuel cell system, and the biomass-fired organic Rankine cycle, all of which have been coupled with a post-combustion, amine-based absorption plant. After these configurations are defined, their performance is discussed. Considerations for optimizing the overall system parameters are identified using the same sources. The paper considers optimization of modifications to the micro gas turbine cycles with exhaust gas recirculation, humidification, and more advanced energy integration for optimal use of waste heat. Related investigations are based largely on numerical studies, with some preliminary experimental work undertaken on the Turbec T100 micro gas turbine. A brief survey is presented of some additional topics, including storage and utilization options, commercially available CC technologies, and direct atmospheric capture. Based on the available literature, it was found that carbon capture for small-scale systems introduces a large energy penalty due to the low concentration of CO2 in exhaust gases. Further development is required to decrease the energy loss from CC for economic feasibility on a small scale. For the micro gas turbine, exhaust gas recirculation, selective gas recirculation, and humidification were shown to improve overall system economic performance and efficiency. However, the highest global efficiencies were achieved by leveraging turbine exhaust waste heat to reduce the thermal energy requirement for solvent regeneration in the CC plant during low- or zero-heating loads. It was shown that although humidification cycles improved micro gas turbine cycle efficiencies, this may not be the best option to improve global efficiency if turbine waste heat is properly leveraged based on heating demands. The biomass-organic Rankine cycle and hybrid micro gas turbine, and solid-state fuel cell systems with CC, are in early developmental stages and require more research to assess their feasibility. However, the hybrid micro gas turbine and solid-state fuel cell energy system with CC was shown numerically to reach high global efficiency (51.4% LHV). It was also shown that the biomass-fired organic Rankine cycle system could result in negative emissions when coupled with a CC plant. In terms of costs, it was found that utilization through enhanced oil recovery was a promising strategy to offset the cost of carbon capture. Direct atmospheric capture was determined to be less economically feasible than capture from concentrated point sources; however, it has the benefit of negative carbon emissions. [ABSTRACT FROM AUTHOR]

Asymmetric thermal zones or even non-rectangular structures are common in residential buildings. These types of structures are not easy to model with specialized programs, and it is difficult to know the heat flows and the relationships between the different variables. This paper presents a methodology for modeling structures with multiple thermal zones using the graph theory arrangement. The methodology allows for generating a mathematical model using all the walls of each thermal zone. The modeling method uses the lumped parameter technique with a structure of two resistors and two capacitors for each thermal zone. The walls and internal surfaces of each zone define the thermal resistances, and the elements for the network structure are created by reducing resistances. The structure selected as a case study is similar to a residential apartment, which demonstrates the possibility of modeling complex and non-traditional structures. The accuracy of the generated mathematical model is verified by comparison with experimental data recorded in a scaled-down model. The reduced model is constructed using a 1:10 ratio with a real apartment. The proposed methodology is used to generate a graph arrangement adjusted to the case study, using the surfaces to build the mathematical model. The experimental data allowed to adjust the simulation results with errors in the range of 1.88% to 6.63% for different thermal zones. This methodology can be used to model different apartments, offices, or non-asymmetric structures and to analyze individual levels in buildings. [ABSTRACT FROM AUTHOR]

The use of solar photovoltaic (PV) generation and battery energy storage (BES) systems in commercial buildings has been increasing significantly in recent years. Most of these systems, however, are designed to solely minimize the investment and operation costs. With the increasing concerns about high-impact low-probability (HILP) events, such as natural disasters, and their impact on power system resilience, there is a substantial need to integrate outage risks in power system infrastructure planning problems. This paper examines the impact of various electricity rates on the feasibility of PV and BES systems in commercial buildings for financial and resilience purposes. Simulation studies are conducted using the Renewable Energy Integration & Optimization (REopt) decision support software to optimize the size of solar PV and BES systems for both financial and resilience purposes, considering different combinations of geographic locations, load profiles, electricity rates, and outage durations. The feasibility assessment is conducted by analyzing and comparing the net present value (NPV) for each combination of parameters. [ABSTRACT FROM AUTHOR]

The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6 , depending on the number of thermal zones studied. [ABSTRACT FROM AUTHOR]

The European Union (EU) has committed to supporting the United Nations' efforts in line with the Paris Agreement for addressing climate change and has set ambitious targets to reduce primary energy consumption and emissions. Similar commitments have also been set by EU-27 member states. For this purpose, it is necessary to use a primary energy factor (PEF) for converting electricity use to primary energy units and for assessing energy conservation measures. Lower PEFs reflect efficiency improvements in power generation, an increased share of renewable energy sources in the fuel mix for electricity generation, and lower transmission and distribution losses. Over the past decades, there have been intensive efforts and notable progress in the EU-27 for increasing the use of renewables in the energy mix for electricity generation. However, the EU default PEF value for electricity was not regularly updated and remained at 2.5 for several years till it was finally recalculated at 2.1 in the 2018 recast of the Energy Efficiency Directive. This paper reviews different calculation options for estimating the PEF for electricity from official annual statistics, presents the historical evolution of the calculated conversion factors, and provides simple linear correlations for projecting the PEF values that can be used to facilitate more-realistic forward-looking calculations and assess national energy efficiency, climate change, or decarbonization plans in EU-27 member states. A more detailed analysis and case studies on the impacts of this work are illustrated for Greece and Poland. [ABSTRACT FROM AUTHOR]

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]

The availability of freshwater and energy is a serious issue in remote and islanded areas, especially at a small scale, where there may not be the possibility to access the grid and/or water distribution systems. In this context, polygeneration systems operating on the basis of local, renewable energy sources can be an answer to the users' demand for electricity, heating, cooling, and domestic hot water. The scope of the proposed paper was to investigate, numerically, the energy and economic feasibility of a novel hybrid polygeneration system powered by biomass, solar, and wind energy for a micro-district of households. The proposed system consists of a biomass-fueled steam cycle, wind turbine, photovoltaic field coupled with thermal and electrical energy storage, adsorption chiller, and a reverse osmosis water desalination unit. The system is also assisted by an LPG generator set running as backup. The system provides space heating and cooling, electrical energy, and fresh and domestic hot water to 10 households located on Pantelleria Island, Italy. The proposed system is modelled and simulated through TRNSYS software with realistic user demand. The energy and economic performance of the proposed system are assessed with respect to a reference system in different scenarios, taking into account islanded operation, connection to the grid, and biomass tariffs. The results show that the proposed system achieves an excellent primary energy saving performance in all the investigated scenarios, with savings of more than 94% for all the investigated scenarios. Excluding any kind of funding, in case of new investment for the system, the simple payback oscillates between 7 and 12 years, showing that the developed alternative is fairly valid with respect to traditional solutions. [ABSTRACT FROM AUTHOR]

Energy-efficient and sustainable technologies are necessary to lower energy and carbon footprints. Many technologies are being pursued to meet the increasing energy demand in buildings. An attractive option is efficient utilization of available energy resources, including renewables, to support current and future building energy needs while targeting grid resiliency, energy, and environmental security at an affordable cost via on-site cogeneration-based approaches. This must include energy-efficient technologies with lower greenhouse gas emissions and optimized cost, performance, and reliability. This paper presents the economic and environmental benefits associated with power technologies such as thermionics and solid oxide fuel cells. Hybrid configurations consisting of heat pumps, power systems, and renewable photovoltaics in cogeneration and trigeneration modes of operation are presented. The role of such technologies in lowering CO2 emissions while improving energy resiliency and serving the needs of underprivileged communities is discussed. The key barriers of affordability and potential solutions for large-scale implementation of these promising technologies are reviewed. Case studies demonstrating the influence of power rating, electrical efficiency, design configuration, carbon dioxide intensity of the grid, and fuel on annual greenhouse gas emissions are presented for residential and commercial buildings. [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]

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]

Distributed electric power production by small-scale customers is increasing continuously. Photovoltaic production is a popular method of producing self-energy for customers. Additionally, power systems require more flexibility when weather-dependent renewable energy production increases. Small-scale customers can increase the self-consumption of self-produced energy by using batteries or a demand response operation. However, batteries require high investment, and demand response operations induce a loss of comfort. Customers who heat their buildings using electric heaters are a good target for demand response operations because their heating can be controlled with limited changes in the indoor temperature. The demand response potential of a building can be defined by simply using customer load profiles and knowledge of the outdoor temperature. Any other information is not required in the proposed novel method. A tolerable variation in indoor temperature corresponds to considerably smaller battery capacity, though it is still a significant amount. With an optimally sized photovoltaic system, it is possible to use both methods simultaneously to increase self-consumption. Maximal benefits can be attained from both methods if the battery system is used as a primary control and the demand response is used as a secondary control. The defined novel method for determining the demand response potential of small-scale customers can also be used when estimating the flexibility of a large customer group. Small-scale customers together can provide significant flexible capacity when their electrical heating is centrally controlled. [ABSTRACT FROM AUTHOR]

CO2 is the most emitted greenhouse gas and is mainly produced by human activity. In fact, about 75% is emitted in cities and 40% of global carbon emissions is produced by the building sector. Therefore, buildings are very important in terms of CO2 emissions. This importance is also reflected in the works that have been developed on this subject. This manuscript reviews the research that has shown or calculated the amounts of CO2 emitted in buildings. For a better understanding of the scope of the investigations, a classification is presented. With this, it is intended to help researchers interested in this area by summarizing the studies carried out to date on the amounts of CO2 emitted depending on the type of building. [ABSTRACT FROM AUTHOR]

This article examines the multidimensional problem of energy poverty, focusing on its connections to climate change and its manifestation at rural and urban scales across selected European countries and Israel. The study examined 31 locations in eight countries with diverse geographical and economic backgrounds: Bosnia and Herzegovina, Greece, North Macedonia, The Netherlands, Portugal, Spain, Slovakia, and Israel. The article aims to understand how winter energy vulnerability in rural and urban locations in these countries could be identified using selected energy poverty indicators and how it evolves under the influence of climate change. A set of sociodemographic, infrastructural, and economic variables, combined with climate analysis, were selected and assessed for their impact on energy poverty. We found that energy poverty in most countries depends significantly on location and regional development. Due to a combination of factors influencing energy poverty, rural households tend to be more vulnerable. Furthermore, climate change consequences will likely leave rural areas more likely to experience energy poverty in the future. [ABSTRACT FROM AUTHOR]

Building-level electrical distribution systems comprise a myriad of current-carrying equipment, conversion devices, and protection devices that deliver power from the utility or local distributed energy resources to end-use building loads. Electric power has traditionally been generated, transmitted, and distributed in alternating current (AC). However, the last decade has seen a significant increase in the integration of native direct current (DC) equipment that has elevated the importance of DC distribution systems. Numerous studies have comparatively examined the performance of various electrical distribution systems in buildings but have failed to achieve uniform conclusions, primarily because of a lack of consistent and analogous performance evaluation methods. This paper aims to fill this gap by providing a standard set of metrics and measurement boundaries to consistently evaluate the performance of AC, DC, or hybrid AC/DC electrical distribution systems. The efficacy of the proposed approach is evaluated on a representative medium-sized commercial office building model with AC distribution and an equivalent hybrid AC/DC and DC distribution model, wherein the AC distribution model is concluded to be the most efficient. The simulation results show variation in computed metrics with different selected boundaries that verify the effectiveness of the proposed approach in ensuring consistent computation of the performance of building-level electrical distribution systems. This paper provides an initial set of guidelines for building energy system stakeholders to adopt appropriate solutions, thus leading to more efficient energy systems. [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]

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]

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]

The integration of renewable energy and flexible energy sources in buildings brings numerous benefits. However, the integration of new technologies has increased the complexity and despite the progress of optimization algorithms and technologies, new research challenges emerge. With the increasing availability of data and advanced modeling tools, stakeholders in the building sector are actively seeking a more comprehensive understanding of the implementation and potential benefits of energy optimization and an extensive up-to-date survey of optimization in the context of buildings and communities is missing in the literature. This study comprehensively reviews over 180 relevant publications on the management and optimization of energy flexibility resources in buildings. The primary objective was to examine and analyze prior research, with emphasis on the used methods, objectives, and scope. The method of content analysis was used to ensure a thorough examination of the existing literature on the subject. It was concluded that multi-objective optimization is crucial to enhance the utilization of flexible resources within individual buildings and communities. Moreover, the study successfully pinpointed key challenges and opportunities for future research, such as the need for accurate data, the complexity of the optimization process, and the potential trade-offs between different objectives. [ABSTRACT FROM AUTHOR]

Since the middle of the 20th century many any buildings were built without any energy standards and still have a comparably poor energy quality. To obtain an overview of the current thermal quality of buildings in a whole city district, it may be promising to work with thermographic images obtained by unmanned aerial vehicles (UAV). Aerial thermography represents a fast and cost-efficient approach compared to traditional terrestrial thermography. In this paper, we describe an approach to finding thermal bridges on aerial thermographic images and characterizing them in terms of their risk of mold formation, energy losses, retrofit costs, and retrofit benefits. To identify thermal bridge types that can be detected reliably on aerial thermographic images, we use a dataset collected with a UAV in an urban district of the German city of Karlsruhe. We classify and characterize 14 relevant thermal bridge types for the German building cohorts of the 1950s and 1960s. Concerning the criterion of mold formation, thermal bridges of window components, basement ceiling slabs, balcony slabs, floor slabs, and attics are found to be particularly relevant to retrofit projects. Regarding energy savings, the retrofit of thermal bridges of window sills, window lintels, and attics shows high potential. The retrofit of attics seems to be less attractive, when also taking into account the necessary retrofit costs. [ABSTRACT FROM AUTHOR]

In cold climatic regions such as those located across Canada, it is necessary to implement heating system technology that is ultra-efficient and that has near-zero rates of emissions. Such systems would satisfy consumers' energy needs and also comply with environmental standards, especially because the systems would account for more than 80% of residential energy use. This paper investigates two complementary efficient systems that can support these heating systems; ground-source heat pumps (GSHPs) and organic Rankine cycle systems (ORCs). The study proposes to couple these two systems in a parallel configuration. A dynamic simulation model created in TRNSYS platform has been deployed to assess the performance of the combined ORC-GSHP based micro-cogeneration system. This former provides heating to a residential house during the heating mode as needed. It has the capacity to switch to a charging mode, during which the ORC system is directly coupled to the ground heat exchanger (GHE), which works as a thermal energy storage and supplies energy to the GSHP. The feasibility of this combined system arrangement, and its comparison with a conventional GSHP system are examined for use in residential buildings in three cities across the varied climatic regions within Canada, namely Edmonton (AB), Halifax (NS), and Vancouver (BC). Results showed that the proposed micro-cogeneration system recorded less energy use of over 80%. The addition of the ORC system had a definite effect on the performance of the GSHP in that it decreased the operating hours from 11–58% compared to the conventional GSHP case and maintained consistently higher COP values. These results may help to specify viable ORC-GSHP based micro-co/trigeneration systems in cold climatic applications and should be useful for prototype design and development. [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]

Data collection and large-scale urban audits are challenging and can be time consuming processes. Geographic information systems can extract and combine relevant data that can be used as input to calculation tools that provide results and quantify indicators with sufficient spatial analysis to facilitate the local decision-making process for building renovations and sustainability assessment. This work presents an open-access tool that offers an automated process that can be used to audit an urban area in order to extract relevant information about the characteristics of the built environment, analyze the building characteristics to evaluate energy performance, assess the potential for the installation of photovoltaics on available building rooftops, and quantify ground permeability. A case study is also presented to demonstrate data collection and processing for an urban city block, and the relevant results are elaborated upon. The method is easily replicable and is based on open data and non-commercial tools. [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]

Natural ventilation is gaining more attention from architects and engineers as an alternative way of cooling and ventilating indoor spaces. Based on building types, it could save between 13 and 40% of the building cooling energy use. However, this needs to be implemented and operated with a well-designed and integrated control system to avoid triggering discomfort for occupants. This paper seeks to review, discuss, and contribute to existing knowledge on the application of control systems and optimisation theories of naturally ventilated buildings to produce the best performance. The study finally presents an outstanding theoretical context and practical implementation for researchers seeking to explore the use of intelligent controls for optimal output in the pursuit to help solve intricate control problems in the building industry and suggests advanced control systems such as fuzzy logic control as an effective control strategy for an integrated control of ventilation, heating and cooling systems. [ABSTRACT FROM AUTHOR]

This article presents causal recipes leading to high and low energy consumption efficiency performances using fuzzy set Qualitative Comparative Analysis (fsQCA). The study found that several causal conditions are sufficient for high energy efficiency performance, including several fiscal and financial incentive policies, a highly educated population, many completed dwellings, and low GDP. The study also found that high inequality in completed dwellings and completed reconstructions, coupled with a low GDP and a low number of policies, lead to high energy consumption efficiency performance. In addition, the analysis showed slight differences between the yearly consistencies, suggesting that time effects are not a concern. On the other hand, a low education level, Gini coefficient, few completed dwellings and reconstructions, coupled with a low number of fiscal and financial policies, are the causal conditions leading to low energy consumption efficiency performance. The study's results suggest that policymakers and stakeholders should consider a combination of several causal conditions when implementing energy efficiency policies. The study also highlights the need for policies focusing on education, fiscal and financial incentives, completed dwellings, and reconstructions to achieve high energy efficiency performance. [ABSTRACT FROM AUTHOR]

Thermal storage is very relevant for technologies that make thermal use of solar energy, as well as energy savings in buildings. Phase change materials (PCMs) are positioned as an attractive alternative to storing thermal energy. This review provides an extensive and comprehensive overview of recent investigations on integrating PCMs in the following low-temperature applications: building envelopes, passive systems in buildings, solar collectors, solar photovoltaic systems, and solar desalination systems. Moreover, techniques for improving heat transfer in PCM systems are described. All applications studies indicate that all applications improve their performance when applying a PCM. One of the most beneficiated technologies is the combined PV-Thermal systems (PVT), where some authors reported an increase in overall efficiency greater than 40%. [ABSTRACT FROM AUTHOR]

The accurate prediction of the solar energy that can be generated using the rooftops of buildings is an essential tool for many researchers, decision makers, and investors for creating sustainable cities and societies. This study is focused on the development of an automated method to extract the useable areas of rooftops and optimize the solar PV panel layout based on the given electricity loading of a building. In this context, the authors of this article developed two crucial methods. First, a special pixel-based rooftop recognition methodology was developed to analyze detailed and complex rooftop types while avoiding the challenges associated with the nature of the particular building rooftops. Second, a multi-objective enveloped min–max optimization algorithm was developed to maximize solar energy generation and minimize energy cost in terms of payback based on the marginal price signals. This optimization algorithm facilitates the optimal integration of three controlled variables—tilt angle, azimuth angle, and inter-row spacing—under a non-linear optimization space. The performance of proposed algorithms is demonstrated using three campus buildings at the University of Alberta, Edmonton, Alberta, Canada as case studies. It is shown that the proposed algorithms can be used to optimize PV panel distribution while effectively maintaining system constraints. [ABSTRACT FROM AUTHOR]

Integrating data analytics, optimisation and dynamic control to support energy services has seen significant interest in recent years. Larger appliances used in an industry context are now provided with Internet of Things (IoT)-based interfaces that can be remotely monitored, with some also provided with actuation interfaces. The combined use of IoT and edge computing enables connectivity between energy systems and infrastructure, providing the means to implement both energy efficiency/optimisation and cost reduction strategies. We investigate the economic implications of harnessing IoT and edge/cloud technologies to support energy management for HVAC (Heating, Ventilation and Air Conditioning) systems in buildings. In particular, we evaluate the cost savings for building operations through energy optimisation. We use a real use case for energy optimisation as identified in the EU "Sporte2" project (focusing on the energy optimisation of sports facilities) and explore several scenarios in relation to costs and energy optimisation. [ABSTRACT FROM AUTHOR]

As a result of the increase in city populations, and the high energy consumption and emissions of buildings, cities in general, and buildings in particular, are the focus of attention for public organizations and utilities. Heating is among the largest consumers of energy in buildings. This study examined the influence of the income of inhabitants on the consumption of energy for heating and the CO2 emissions in city buildings. The study was carried out using equivalized disposable income as the basis for the analysis and considered the economies of scale of households. The results are shown per inhabitant and household, by independently considering each city. Furthermore, to more clearly identify the influence of the population income, the study was also carried out without considering the influence of the climate. The method was implemented in the case of Spain. For this purpose, Spanish cities with more than 50,000 inhabitants were analyzed. The results show that, both per inhabitant and per household, the higher the income of the inhabitants, the greater the consumption of energy for heating and the greater the emissions in the city. This research aimed to help energy utilities and policy makers make appropriate decisions, namely, planning for the development of facilities that do not produce greenhouse gases, and enacting laws to achieve sustainable economies, respectively. The overall aim is to achieve the objective of mitigating the impact of emissions and the scarcity of energy resources. [ABSTRACT FROM AUTHOR]

Phase change materials have been overwhelmingly used for thermal energy storage applications. Among organics, fatty acids are an important constituent of latent heat storage. Most of the saturated fatty acid PCMs so far studied are either unary or binary constituents of pure fatty acids. In the present study, ternary blends of saturated fatty acids i.e., capric, lauric, myristic, stearic, and palmitic acids have been developed with different weight proportions. A series of 28 ternary blends viz. CA-LA-MA, CA-LA-PA, CA-LA-SA, CA-MA-PA, CA-MA-SA, and CA-PA-SA were prepared and analyzed with differential scanning calorimetry, thermal gravimetric analysis, and Fourier transform infrared spectroscopy. DSC analysis revealed that the prepared materials lie in the 15–30 °C temperature range. Also, 300 thermal melt/freeze cycles were conducted which showed ±10% variation in terms of the melting peak for most of the PCMs, with the average latent heat of fusion between 130 and 170 kJ/kg. The TGA analysis showed that most of the PCMs are thermally stable up to 100 °C and useful for medium-low storage applications, and FTIR analysis showed that the materials are chemically stable after repeated thermal cycles. Based on cycle test performances, the developed materials were found to be reliable for long-term use in building and photovoltaic applications. [ABSTRACT FROM AUTHOR]