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Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively). [ABSTRACT FROM AUTHOR]

The demand for low-carbon and energy-efficient building designs is urgent, especially considering that building energy consumption constitutes a significant part of global energy usage. Unlike small to medium-sized buildings such as residential and office spaces, large public buildings, like sports facilities, have unique usage patterns and architectural forms, offering more significant potential for energy-saving strategies. This review focuses on sports buildings, selecting 62 high-quality papers published in building science over the past 30 years that investigate low-carbon and energy-efficient research. Summarizing and synthesizing these papers reveals that current studies predominantly concentrate on four main areas: indoor air quality, ventilation, thermal environment, and energy consumption. Notably, many studies emphasize improving indoor thermal comfort and reducing energy consumption in sports buildings through measurements and evaluations of indoor thermal environments, temperature distributions, heat transfer phenomena, and energy consumption analyses. Key outcomes indicate that green technology innovations, such as energy substitution technologies, significantly enhance energy efficiency and reduce CO2 emissions. However, present research emphasizes singular energy-saving approaches, suggesting future directions could integrate comprehensive green technologies, life-cycle assessments, and applications of intelligent technologies and the Internet of Things (IoT). These enhancements aim to provide more effective and sustainable solutions for implementing green, low-carbon energy practices in sports buildings. The review emphasizes that in order to accomplish sustainable urban growth and achieve global carbon neutrality targets, a comprehensive approach involving technical innovation, legislative assistance, and extensive preparation is crucial. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs' designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide a holistic review of two decades of research advancement in the indoor environmental quality modelling and indexing field (IEQMI) using bibliometric analysis methods. The explicit objectives of the present study are: (1) identifying researchers, institutions, countries (territories), and journals with the most influence in the IEQMI topic; (2) investigating the hot topics in the IEQMI field; and (3) thematically analysing the keyword evolution in the IEQMI field. A scientometric review was conducted using the bibliometric data of 456 IEQMI research articles published in the past two decades. VOSviewer software was employed for bibliometric analysis, and the SciMAT tool was used to investigate the keywords' thematic evolution in three sub-periods (2004–2009; 2010–2015; 2016–2021). Results show that there is a continuous increment in the number of published papers in the field of IEQMI, and 60 out of 193 countries in the world have been involved in IEQMI studies. The IEQMI research mainly focuses on: (a) thermal comfort and energy efficiency; (b) occupant satisfaction and comfort; (c) IAQ and health issues; (d) methods and procedures. This field has undergone significant evolution. While 'indoor environmental quality was initially the only theme in the first period', 'occupant satisfaction', 'buildings', 'impact', 'building information modelling', and 'health' were added as the main thematic areas in the second period; 'occupant behaviour' and 'energy' were novel themes in IEQMI studies receiving much attention in the third period. [ABSTRACT FROM AUTHOR]

Countless years have been spent researching the strategies necessary for improving the energy consumption of buildings globally. There have been numerous attempts at achieving both passive solutions and enhancing and optimising the existing active systems. This paper seeks to review, analyse and summarise the possibilities of using thermoelectricity in two different contexts to the integration with buildings, integrated thermoelectric systems, and non-integrated thermoelectric systems. The utilisation of thermoelectricity in cohorts with existing renewable technologies and the utilisation of thermoelectric systems that operate individually, both have the potential to provide the occupants of a building with conditions pertinent to thermal and visual comfort. The results in this paper are classified according to the integration types of thermoelectric systems within different parts of the fabric of a building while maintaining an active role in enhancing the building envelope and self-contained thermoelectric systems that sustain a passive role for the same. The introduction to this paper also gives a very broad and surface-level insight into categorisation of different kinds of thermoelectric systems that are being studied and researched across the world. [ABSTRACT FROM AUTHOR]

The pursuit of climate neutrality requires global systemic actions involving the use of solutions aimed at reducing emissions. Changes must be introduced in all sectors affecting climate change, namely power engineering and district heating, construction, transport, and industry, as well as agriculture and forestry. Analyzing the structure of final energy consumption in the EU by sector, it can be stated that households account for 27% of the total energy consumption. Comprehensive actions are needed to increase the energy efficiency of buildings. The aim of this paper was to indicate aspects of improving energy efficiency in buildings and their equipment, taking into account the striving for climate neutrality. Analyzed possibilities and conditions of using various solutions of energy-efficient systems aimed at increasing energy resilience and security and preventing environmental degradation. Particular attention was paid to construction and material solutions, as well as installation solutions, which increased the accumulation and energy efficiency of the building. These activities are closely related to the conditions and dynamics of the heat exchange process in the applied solutions and are also related to the factors influencing thermal comfort and energy consumption in buildings. Due to the growing popularity of modern information technologies and artificial intelligence in energy management in recent years, this article reviews the latest research in this area. One of the directions of future research indicated by scientists is autonomous building control in real time, adapting to the momentary needs of users. The analysis of the possibilities of using modern energy efficiency solutions in buildings conducted in this work may be useful for optimizing heat and energy management models and models of society's consumption as an element of energy transformation towards climate neutrality and counteracting the deepening of energy poverty. [ABSTRACT FROM AUTHOR]

The thermal performance of windows is an important area of research to reduce the energy consumption of buildings and improve indoor comfort. The application of innovative glazing technologies can improve the energy performance of windows and transparent facades, resulting in significant energy savings. This paper presents research results on the energy performance of electrically heated windows. A comprehensive CFD and experimental analysis of the heat transfer processes in a window space depending on the size, power, and location of an electric heater was performed. The convective gas flows in the gas gaps and in the boundary layer were also analysed, and it is shown that a window with an electric heater can reduce the energy consumption of a room by 10–12%. This study is a pilot study to assess the feasibility and cost-effectiveness of electric local heating of a window or facade to minimise heat loss before full-scale implementation. The results of numerical modelling and experimental studies confirm the potential of the new technologies. [ABSTRACT FROM AUTHOR]

The energy challenges of overcoming climate change and economic and global political crises require not only the increased integration of renewable energies but also an optimisation of energy generation and use and, as a result, a reduction in energy consumption in various sectors. Thermal energy consumption in buildings in particular accounts for a significant proportion of final energy consumption. With respect to commercial buildings, a central problem in optimising the system settings is the lack of or only limited information about the actual room temperatures as well as the comfort requirements and temperature perception of the users in the rooms on the one hand and the operational management and settings specified by the facility management, for example, the heating curves of the heating circuits, on the other. The aim here is to create a bidirectional flow of data and information so that the compromise between the necessary room temperatures and the comfort of the users and the most energy-efficient operational management possible can be achieved. In this context, the paper presents a developed and tested web platform that makes it possible to optimise the operation of the system technology, particularly the heating system in the building, from an energy point of view and to involve the building user (e.g., office employees) and to pass on information to the facility management, thus pursuing a holistic approach. In the associated Living Lab project (called ComfortLab), it was possible to obtain over 6500 votes on temperature perception and combine this with building operation and the parameters relevant to facility management. This made it possible to bridge the gap between user requirements and room temperatures on the one hand and energy consumption and the inlet temperature of the heating system and supply circuits on the other. The use of the platform makes it possible to optimise the setpoint specification, specifically the inlet temperature of individual heating circuits, considering both regular building operation at times of presence and the setting of weekend and night setback times. The results show a diversified picture regarding temperature perception and possible room temperature reductions of several degrees Celsius and energy savings in the double-digit percentage range. [ABSTRACT FROM AUTHOR]

The growth of electric vehicles (EVs) and their integration into existing and future buildings bring new considerations for energy efficiency (EE) and balance when combined with renewable energy. However, for buildings with an energy efficiency label, such as Near Zero Energy Building (NZEB) or Positive Energy Building (PEB), the introduction of EVs may result in the declassification of the EE label due to the additional energy required for the charging infrastructure. This underscores the increasing relevance of demand-side management techniques to effectively manage and utilize energy consumption and generation in buildings. This paper evaluates the influence of electric vehicle (EV) charging on NZEB/PEB-labeled buildings of the Brazilian Building Labeling Program (PBE Edifica). Utilizing on-site surveys, computational modeling, and thermos-energetic analysis with software tools such as OpenStudio v. 1.1.0 and EnergyPlus v. 9.4.0, an energy classification was conducted in a building in the city of Belem, State of Para, Brazil. Subsequently, power flow simulations employing probabilistic models and Monte Carlo approaches were executed in the OpenDSS software v. 10.0.0.2 to examine the impact of EV integration, both with and without the implementation of demand-side management techniques. Analyses using the labeling methodology demonstrated that the building has EE level C and NZEB self-sufficiency classification. The assessment of the impact of EV integration on the building's total energy consumption in the base (current) scenario was carried out in two scenarios, with (2) and without (1) supply management. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 resulted in a smaller increase in energy consumption of 40.50%, and guaranteed the return of the NZEB class lost in scenario 1, but it was not enough to return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies, as the findings show that the two scenarios present a difference of 41.55% in energy consumption. Nonetheless, these strategies are not enough if other consumption restrictions or energy efficiency measures are not applied to other building systems. [ABSTRACT FROM AUTHOR]

A large proportion of building energy consumption in tropical countries like Thailand primarily comes from air conditioning systems used to maintain the comfort level of building occupants. This paper aims to evaluate the performance of an alternative cooling system based on the evaporative principle in terms of thermal characteristics and energy consumption. A simulation model using computational fluid dynamics (CFD) software ANSYS version 16.0 and an actual experimental setup at the laboratory level were built to verify the results of the proposed cooling system. Additionally, factors that influence performance, such as components of the building envelope and the building's orientation, are considered. This research aims to demonstrate the impact of building envelope material and building characteristics on the energy usage in air conditioning systems and to propose an energy-efficient cooling system. The results demonstrate that the proposed cooling system can reduce the temperature inside the building. However, the characteristics of the building also affect the energy performance. Thus, the proposed cooling system, in combination with an efficient envelope material, can achieve energy savings of around 35–43%. Therefore, a combination of the proposed cooling system and optimal building design can ensure comfort for building occupants while saving energy. [ABSTRACT FROM AUTHOR]

Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies' energy management. In this paper, we propose a novel hybrid forecasting model designed to predict load series in multiple households. Our proposed method integrates multivariate variational mode decomposition (MVMD), the whale optimization algorithm (WOA), and a temporal fusion transformer (TFT) to perform one-step forecasts. MVMD is utilized to decompose the load series into intrinsic mode functions (IMFs), extracting characteristics at distinct scales. We use sample entropy to determine the appropriate number of decomposition levels and the penalty factor of MVMD. The WOA is utilized to optimize the hyperparameters of MVMD-TFT to enhance its overall performance. We generate two distinct cases originating from BCHydro. Experimental results show that our method has achieved excellent performance in both cases. [ABSTRACT FROM AUTHOR]

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building's exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs. [ABSTRACT FROM AUTHOR]

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings' owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves. [ABSTRACT FROM AUTHOR]

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems' performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI's and Elsevier's databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields. [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]

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems. [ABSTRACT FROM AUTHOR]

In the context of an insulated area with a subtropical climate, such as La Réunion island, it is crucial to reduce the energy consumption of buildings and develop local renewable energy sources to achieve energy autonomy. Direct current (DC) nanogrids could facilitate this by reducing the energy conversion steps, especially for solar energy. This article presents the deployment and efficiency evaluation of a 48 VDC low-voltage direct current (LVDC) nanogrid, from conception to real-world installation within a company. The nanogrid consists of a photovoltaic power plant, a lithium–iron–phosphate (LFP) battery, and DC end-use equipment, such as LED lighting and DC fans, for two individual offices. For identical test conditions, which are at an equivalent cabling distance and with the same final power demand, the total power consumed by the installation is measured for several stages from 50 to 400 W, according to a 100% DC configuration or a conventional DC/AC/DC PV configuration incorporating an inverter and AC/DC converter. The methodology used enables a critical view to be taken of the installation, assessing both its efficiency and its limitations. Energy savings of between 23% and 40% are measured in DC for a power limit identified at 150 W for a distance of 25 m. These results show that it is possible to supply 48 VDC in an innovative way to terminal equipment consuming no more than 100 W, such as lighting and air fans, using the IEEE 802.3 bt power over ethernet (PoE) protocol, while at the same time saving energy. The nanogrid hardware and software infrastructure, the methodology employed for efficiency quantification, and the measurement results are described in the paper. [ABSTRACT FROM AUTHOR]

The building sector plays an important role in the global climate change mitigation objectives. The reduction of CO2 emissions and energy consumption in the building sector has been intensively investigated in the last decades, with solar thermal energy considered to be one of the most promising solutions due to its abundance and accessibility. However, the discontinuity of solar energy has led to the study of thermal energy storage to improve the thermal performance of solar thermal systems. In this review paper, the integration of various types of phase-change materials (PCMs) in transpired solar collectors (TSC) is reviewed and discussed, with an emphasis on heat transfer enhancements, including nanomaterials. Thermal energy storage applied to TSC is studied in terms of design criteria, materials technologies, and its impact on thermal conductivity. This review highlights the potential of nanomaterial technology integration in terms of thermal performance improvements. The utilization of nanomaterials in solar walls holds the potential to significantly enhance their performance. The integration of diverse materials such as graphene, graphite, metal oxides, and carbon nanoparticles can pave the way for improving thermal conductivity. [ABSTRACT FROM AUTHOR]

The city of Amsterdam has ambitious goals to achieve a 95% reduction in carbon emissions by 2050 and to phase out natural gas by 2040. Disconnecting the building stock from natural gas requires well-ventilated and well-insulated buildings and a switch to renewable energy sources, making optimal use of heat pumps and sustainable heating solutions available locally. Most buildings in the historical city centre are protected and often insufficiently insulated, leading to increased energy use and a poor thermal environment. Standard retrofitting interventions may be restricted, requiring new approaches to balancing the need for energy efficiency and the preservation of heritage significance. With the case of the Amsterdam City Centre, the goal of this research is to present a parametric modelling approach for energy retrofitting heritage buildings and to identify minimum requirements for preparing the residential stock to lower temperature heat (LTH). Using parametric design and bottom-up energy modelling, the research estimates that a 69.1% of natural gas reduction could be achieved when upgrading the buildings to lower temperature (LT). Results of this paper also demonstrate how the applied approach can be used to guide decisions on the improvement in energy performance of the historic built environment. [ABSTRACT FROM AUTHOR]

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP. [ABSTRACT FROM AUTHOR]

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly. [ABSTRACT FROM AUTHOR]

The need to effectively control and reduce energy consumption in buildings has become a global concern, prompting an increasing number of studies on the energy efficiency of straw buildings. However, previous review articles on straw research have primarily focused on fragmented material properties such as thermal insulation and mechanical strength and have lacked a comprehensive review of straw materials in building energy efficiency, as well as a thorough analysis of the development lineage of straw building materials. To fill this research gap, this study conducted a bibliometric analysis of 338 papers on the energy efficiency of straw materials published in the WOS core database between 1992 and 2022. The study constructed and visualized multifaceted co-occurrence networks representing the research literature on the energy efficiency of straw building materials, providing a comprehensive understanding of current research efforts, development trends, hot research directions, and the development lineage of this field since 1992. The study's conclusions suggest that the next research hotspots in this area will be the whole life-cycle of straw materials and their compounding, performance, and application to construction. By tracing the development lineage and clarifying the relationship between the macroscopic building environment and microscopic straw materials, this study offers better predictions of the future development prospects of straw buildings. These findings provide researchers with valuable insights into current research efforts and future research directions in this field, while also serving as a reference for governments seeking to formulate relevant policies for the energy-efficient design of buildings made of straw materials. [ABSTRACT FROM AUTHOR]

Domestic water heating accounts for 15% to 27% of the total energy consumption in buildings in Australia. Over the past two decades, the latent heat thermal energy storage (LHTES) system has been widely investigated as a way to reduce fossil fuel consumption and increase the share of renewable energy in solar water heating. However, the research has concentrated on the geometric optimisation of the LHTES heat exchanger for the past few years, and this might not be sufficient for commercialisation. Moreover, recent review papers mainly discussed the development of a particular heat-transfer improvement technique. This paper presents perspectives on various solar hot water systems using LHTES to shift focus to on-demand performance studies, as well as structure optimisation studies for faster commercialisation. Future challenges are also discussed. Since the topic is an active area of research, this paper focuses on references that showcase the overall performance of LHTES-assisted solar hot water systems and cannot include all published work in the discussion. This perspective paper provides directional insights to researchers for developing an energy-efficient solar hot water system using LHTES. [ABSTRACT FROM AUTHOR]

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model's performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. [ABSTRACT FROM AUTHOR]

Although the energy and cost benefits for retrofitting existing buildings are promising, several challenges remain for accurate measurement and verification (M&V) analysis to estimate these benefits. Due to the rapid development in advanced metering infrastructure (AMI), data-driven approaches are becoming more effective than deterministic methods in developing baseline energy models for existing buildings using historical energy consumption data. The literature review presented in this paper provides an extensive summary of data-driven approaches suitable for building energy consumption prediction needed for M&V applications. The presented literature review describes commonly used data-driven modeling approaches including linear regressions, decision trees, ensemble methods, support vector machine, deep learning, and kernel regressions. The advantages and limitations of each data-driven modeling approach and its variants are discussed, including their cited applications. Additionally, feature engineering methods used in building energy data-driven modeling are outlined and described based on reported case studies to outline commonly used building features as well as selection and processing techniques of the most relevant features. This review highlights the gap between the listed existing frameworks and recently reported case studies using data-driven models. As a conclusion, this review demonstrates the need for a flexible M&V analysis framework to identify the best data-driven methods and their associated features depending on the building type and retrofit measures. [ABSTRACT FROM AUTHOR]

The direct release of energy from the groundwater under the building of the Sobieski Family Palace in Lublin, Poland, and the obtainment of heat and cooling energy for Heating, Ventilation, and Air Conditioning systems (HVAC) provide an opportunity to reduce electricity and heat consumption and to limit CO2 emissions by 15–50%. The upgrade to the Sobieski Family Palace and the addition of new educational and administrative functions require state-of-the-art, energy-saving, and environmentally friendly solutions such as Fuel Cells and Hydrogen for Heating, Ventilation, and Air Conditioning (FCH HVAC) systems. As part of the program "Research for high-quality air in architecture and urban studies," carried out since 2018 at the Institute of Architecture Planning, Department of Contemporary Architecture, Faculty of Civil Engineering and Architecture of the Lublin University of Technology, in 2021, further research was carried out in order to prepare for the potential implementation in 2022 of the project entitled "Development and Upgrade of the Former Sobieski Family Palace in Lublin. The main body of the building—educational and administrative function," whose chief designer was Architect J. Wrana, Lublin University of Technology, 2021. The objective of this paper is to identify technologies and solutions specifically designed for HVAC systems in upgraded and renovated historic buildings. This paper is also a call for cooperation among institutions, scientists, higher education institutions, as well as an expression of appreciation for the immense energy stored in groundwater. This energy not only has the lowest carbon footprint but also is the only generally accessible large storage source from which we were unable to obtain ecologically pure energy before the introduction of FCH technology. [ABSTRACT FROM AUTHOR]

The energy industry has been trying to reduce the use of fossil fuels that emit carbon and to proliferate renewable energy as a way to respond to climate change. The attempts to reduce carbon emissions resulting from the process of generating the electric and thermal energy needed by a building were bolstered with the introduction of the concept of nZEB (nearly zero-energy building). In line with such initiatives, the South Korean government made it mandatory for new buildings to have an nZEB certificate as a way to promote the supply of renewable energy. The criteria for Energy Independence Rate, which is one of the nZEB certification criteria in South Korea, is to maintain the share of renewable energy as at least 20% of the primary energy sources for the building. For a new building in South Korea to have an nZEB certificate, it is required to establish an energy plan that would allow the building to meet the Energy Independence requirement. This optimally reflects the cost of installation for renewable energy facilities and the cost of purchasing energy from external sources, such as the national grid or district heating companies. In South Korea, the base retail rate of energy is calculated based on the peak demand per hour over the year, rather than the contracted energy. This has produced difficulties in standardizing the process with a mathematical model; in addition, there have not been many preceding studies that could be used as a reference. In this regard, this paper analyzed a modeling strategy for developing a realistic yet optimized energy plan in consideration of the unique conditions of the retail energy rates of South Korea, and analyzed the impact of the rates based on peak demands upon the total energy plan. In this study, our research team analyzed the electric billing system, conducted a case study, and analyzed the impact of the billing system that is based on the peak demand upon the optimal cost. By utilizing the restrictions for reaching the 20% Energy Independence goal, this paper calculated the proper energy supply facility capacity for renewable energy. Then, the cases in which the maximum demand modeling was used and the cases without one were compared to confirm the cost benefits observable when the suggested model is added or implemented. [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 sixth paper of this special issue, by Chiosa et al. [[6]], proposes an innovative anomaly detection and diagnosis methodology to automatically detect anomalous energy consumption in buildings, in addition to performing a diagnosis on the sub-loads that are responsible for anomalous patterns. The third paper in this special issue, by Neshat et al. [[3]], presents an optimization framework of a multi-mode wave energy converter to be tested in a small island in the west of Sicily, Italy, in the Mediterranean Sea. In the next paper, Henrich et al. [[7]] analyse the impact of energy models in decision making processes for energy transitions in ten municipalities in the Netherlands. [Extracted from the article]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

This paper presents the challenges of increasing the energy efficiency investments in European Union (EU) residential buildings in the context of achieving climate neutrality by 2050. The paper presents the results of the PRIMES buildings model in key energy policy applications to support cost-effective and fair policy making in buildings across Europe. The model covers, in detail, the building sector for all the EU Member States (MS), segmenting the buildings into many categories. The approach proposed includes non-market barriers in conventional microeconomic modelling, which combined with idiosyncratic preferences can capture poor energy efficiency choices and still represent rational behaviours. The model includes a detailed portrayal of policies specific to the sector, comprising economic and regulatory policies as well as institutional measures. The results of the model show that the removal of non-market barriers is of great importance in reducing energy consumption and increasing both the pace and the depth of renovation investment. However, the institutional measures alone are not enough to induce energy efficiency improvement to the scale required to achieve the climate neutrality objectives. Economic (i.e., subsidies) or regulatory measures (i.e., energy performance standards) are also required to decrease emissions and energy consumption in buildings and the paper compares different configurations thereof. The optimum policy mix obviously derives from a compromise among various aims including the cost-effectiveness of the policy budget and the distributional impacts across building and consumer types. [ABSTRACT FROM AUTHOR]

The energy consumption of buildings is a significant contributor to overall energy consumption in developed countries. Therefore, there is great demand for intelligent buildings in which energy consumption is optimized. Online control is a crucial aspect of such optimization. The implementation of modern algorithms that take advantage of developments in information technology, artificial intelligence, machine learning, sensors, and the Internet of Things (IoT) is used in this context. In this paper, an architecture for testing central heating control algorithms as well as the control algorithms of the heating system of the building is presented. In particular, evaluation metrics, the method for seamless integration, and the mechanism for real-time performance monitoring and control are put forward. The proposed tools have been successfully tested in a residential building, and the conducted tests confirmed the efficiency of the proposed solution. [ABSTRACT FROM AUTHOR]

This paper aimed at assessing the technical and economic potential of using rooftop solar photovoltaic (PV) systems in Lithuanian urban areas to support energy and climate policy formation and its implementation in the country. A bottom-up approach was applied. A number of apartment (AP), commercial (COM) and public (PUB) buildings, electric vehicle (EV) charging infrastructures and solar PV systems in the largest Lithuanian cities were analyzed, and the results were tailored to national-wide estimates. LiDAR and photogrammetry data of building topologies, geographical information systems (GIS), digital maps for rooftop measurements, factual solar photovoltaic (PV) performance modeling, average profiles of solar PV systems, statistical data of energy consumption in buildings, levelized cost of energy (LCOE) models and the literature review method were used. The results of the assessment of selected buildings and their categories reveal that the rooftop area per installed PV unit was 14.1–18.3 m2/kW in AP buildings, followed by 18.0–18.6 m2/kW in COM and 6.1–18.3 m2/kW in PUB buildings; the factor of rooftop suitability for solar PV system installation varied in ranges of 31.5–85.4%, 48.9–99.4% and 50.1–88.2%, respectively. Corrected by expert judgement, these estimates served as inputs for the assessment of technical and economic potentials. Furthermore, energy generated by solar PV could be consumed locally with little supplied to the distribution grid, resulting in no or a limited need for expensive investments in infrastructure. In addition, locating an EV charger on the premises of COM or PUB buildings could improve the localized consumption of solar energy. Thus, rooftop solar PV systems in urban Lithuanian areas are preferable. The results of the technical potential analysis show that, on the rooftops of AP buildings, 272 MW could be installed, followed by 78 MW on roofs of PUB and 67 MW on COM buildings. If technical potential is extracted, EUR 332.7 million can be invested. The cost of energy produced by solar PV systems was estimated at 2.34–5.25 EURct/kWh, which is significantly lower than the prices of market and retail electricity, standing at 23 EURct/kWh and 24 EURct/kWh (with support from the government) in 2022, respectively. The research's limitations are defined by the fact that average values were applied to estimate the technical and economic potential of rooftop solar PV systems; a limited number of building categories were analyzed; the category of private houses, which is minor in cities, was missed, and rural areas with a great number of private houses were not covered by this study. [ABSTRACT FROM AUTHOR]

The process of urbanization resulting from population growth is causing a transformation of natural landscapes into built environments, and contributing to a significant rise in air and surface temperatures in urban areas, resulting in what is known as the urban heat island (UHI). Ignoring the UHI effect and use of weather data from open fields and airport locations for energy and thermal comfort analysis can lead to over- and underestimation of heating and cooling loads, improper sizing of equipment, inefficiencies in the mechanical systems operation, and occupants' thermal discomfort. There is a need for computationally efficient urban canopy temperature prediction models that account for the urban morphology and characteristics of the study area. This paper presents the development and application of an artificial neural network (ANN)-based method for generating hourly urban canopy temperature and local wind speed for energy simulation. It was used to predict the urban canopy temperature of a neighborhood in downtown Vancouver and the resulting building energy consumption and indoor temperature in a typical building in the area. The results showed that the UHI effect increased the total cooling energy demand by 23% and decreased the total heating energy consumption by 29%, resulting in an overall negative effect on the total energy demand of the building, which was 18% higher in the urban area. The UHI effect also increased the number of hours of indoor temperature above the cooling set point by 7.6%. The methodology can be applied to determine the urban canopy temperature of neighborhoods in different climate zones and determine the varying urban heat island effects associated with the locations. [ABSTRACT FROM AUTHOR]

To meet the 2050 EU decarbonization goals, there is a need for new and innovative ideas to increase energy efficiency, which includes reducing the energy consumption of buildings and increasing the use of on-site renewable energy sources. One possible solution for achieving efficient thermal energy transition in the building sector is to assign new functionalities to the building envelope. The building envelope can function as a thermal energy storage system, which can help compensate for irregularities in solar energy availability. This can be accomplished by utilizing phase change materials as the energy storage medium in the building envelope. In this paper, two phase change materials with different melting temperatures of 21 °C and 28 °C are compared for their application in a dynamic solar building envelope. Both experimental and numerical studies were conducted within the scope of this study. The laboratory testing involved simulating the conditions of the four seasons through steady-state and dynamic experiments. The performance of the phase change materials was evaluated using a small-scale PASLINK test stand that imitates indoor and outdoor conditions. A numerical model of a small-scale building envelope was created using data from laboratory tests. The purpose of this model was to investigate how the tested phase change materials perform under different climate conditions. The experimental findings show that RT21HC is better at storing thermal energy in the PCM and releasing it into the indoor area than RT28HC. On the other hand, the numerical simulation results demonstrate that RT28HC has an advantage in terms of thermal storage capacity in climates found in Southern Europe, as it prevents overheating of the room. [ABSTRACT FROM AUTHOR]

Air-conditioning systems account for 40–60% of the energy consumption of buildings, and most of this figure corresponds to the cooling and dehumidification process of air-conditioning units. Compared with traditional compressed air-conditioning systems, solid adsorption dehumidification systems possess good potential to improve indoor air quality and reduce buildings' energy consumption. However, there are still some problems that prevent the use of solid adsorption dehumidification systems, such as their complexity and high regeneration temperatures. The key to solving the above problem is the discovery of more suitable adsorbents. In this paper, poly N-isopropylacrylamide/polypyrrole (PNIPAm/PPy) hydrogel was selected as the research object, and the performance of the dehumidification material and its potential for application in solid dehumidification systems were studied. It was found that the pore structure of PNIPAm/PPy was relatively complex and that there were abundant pores with uneven pore sizes. The minimum pore size was about 4 μm, while the maximum pore size was about 25 μm, and the pore sizes were mostly distributed between 8 and 20 μm. Abundant and dense pores ensure good hygroscopic and water-releasing properties of the resulting hydrogel. The PPy inside the hydrogel acts as both a hygroscopic and photothermal agent. In an environment with a relative humidity of 90%, 60%, and 50%, the hygroscopic efficiency of PNIPAm/PPy reached 80% in about 75, 100, and 120 min, and the corresponding unit equilibrium hygroscopic capacity values were 3.85 g/g, 3.72 g/g, and 3.71 g/g, respectively. In the initial stage, the moisture absorption increased with the increase in time; then, the increase in moisture absorption decreased. When the temperature was below 40 °C, the hygroscopic performance of PNIPAm/PPy was almost temperature-independent. The PNIPAm/PPy with different thicknesses showed similar moisture absorption efficiency. The lowest desorption temperature of PNIPAm/PPy was 40 °C, which indicates that low-grade energy can be used for material desorption. And the higher the temperature, the faster the desorption rate of PNIPAm/PPy and the higher the desorption amount. It can be seen that the PNIPAm/PPy hydrogel presents good desorption performance and can be used repeatedly. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO 2 concentration. The validation results show that the model can predict room temperature and CO 2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller. [ABSTRACT FROM AUTHOR]

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers' energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings. [ABSTRACT FROM AUTHOR]

Multifamily buildings account for about half of all residential buildings in Poland and about 70% of the urban population live in them. Most of them require thermal refurbishment. Evaluation of a building's energy performance with the use of correlation models has been very rarely used in relation to buildings in Poland. This method is simple, fast and can improve the decision process on thermal modernisation. It is especially important at the pre-design stage of planned investments. This paper presents an attempt to develop correlations that could be used in the energy assessment of multifamily buildings. For this purpose a dataset containing of 29 audits of multifamily buildings was used. The statistical analysis was performed for buildings before and after planned refurbishment. Obtained relationships showed strong correlation between thermal demand for space heating or domestic hot water and ventilation airflow and thermal transmittance of windows (R2 > 0.6). After designed modernisation the number of strong correlations decreased. Energy for domestic hot water was correlated with the number of inhabitants (R2 = 0.6). Energy for space heating was correlated with the number of users and heated volume of a building. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of public procurement documents in the domain of university buildings taken from the e-procurement platform, particularly focusing on their transformation towards more efficient energy consumption. Using a corpus of the titles of the public procurement documents from 2020 to 2022, we used Latent Dirichlet Allocation (LDA) for topic modeling to understand the key thematic areas of focus. The methodology presented in this study incorporated a bifurcated approach. This two-stage procedure began with preprocessing and dictionary creation from the corpus of titles of procurement documents. Following this, the Latent Dirichlet Allocation (LDA) model was employed for topic extraction and trend analysis, thereby providing a comprehensive understanding of the thematic progression in procurement practices over time. Our analysis revealed a shift in emphasis from modernization towards the adoption of energy-saving technologies as well as a growing focus on broader sustainability initiatives. However, a less prevalent topic was adherence to cooling & heating systems, suggesting potential areas for improvement in procurement practices. These findings contribute to the growing body of knowledge on sustainable procurement in university buildings and offer valuable insight for universities to enhance their energy efficiency strategies. [ABSTRACT FROM AUTHOR]

The Renovation Wave for Europe highlighted the role of the public building stock for which Directive 2012/27/EU has set an annual renewal rate of 3%, which should rise to reach the goal of decarbonization by 2050. In this paper, the energy retrofit of an educational building—at the academic level—in Southern Italy was investigated. The aim was to evaluate the incentive share, which could accelerate the energy efficiency process, to achieve a cost-effective nZEB. The results show that the highest incentive rate is required for interventions on the opaque building envelope, which are also those that allow the least energy savings. An incentive rate of about 45% for the energy efficiency of the transparent envelope is necessary to reduce the payback time by about 7 years. The efficiency of the plants and the installation of a PV system are energetically and economically convenient even without forms of economic incentive. Finally, if the building is brought to high energy standards—a primary energy saving of 46% and energy class A3—an incentive rate of 40% is required to repay the intervention in about 10 years. [ABSTRACT FROM AUTHOR]

The share of residential building energy consumption in global energy consumption has rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption under different indoor and outdoor conditions is an essential step towards improving energy efficiency and reducing carbon footprints in the residential building sector. In this paper, a PSO-optimized random forest classification algorithm is proposed to identify the most important factors contributing to residential heating energy consumption. A self-organizing map (SOM) approach is applied for feature dimensionality reduction, and an ensemble classification model based on the stacking method is trained on the dimensionality-reduced data. The results show that the stacking model outperforms the other models with an accuracy of 95.4% in energy consumption prediction. Finally, a causal inference method is introduced in addition to Shapley Additive Explanation (SHAP) to explore and analyze the factors influencing energy consumption. A clear causal relationship between water pipe temperature changes, air temperature, and building energy consumption is found, compensating for the neglect of temperature in the SHAP analysis. The findings of this research can help residential building owners/managers make more informed decisions around the selection of efficient heating management systems to save on energy bills. [ABSTRACT FROM AUTHOR]

Anastasi et al. [[7]] talk about how COVID-19 has led to an acceleration of the transition process of buildings into smart buildings and active environments, especially with regard to areas such as occupant density and air quality. This paper makes a lot of similar points to some aforementioned studies, particularly [[1], [3]], but the authors stressed the importance of implementing a building performance simulation as a framework for energy-efficient building development. Construction is a key sector for green growth on a global scale. Szul et al. [[5]] conducted a study on the feasibility of using Takagi-Sugeno Fuzzy Modelling to predict the energy efficiency in buildings, particularly in buildings undergoing thermal improvement. [Extracted from the article]

The need for healthy indoor conditions, the energy crisis, and environmental concerns make building ventilation systems very important today. The elements of ventilation systems to reduce energy intensity are constantly the subject of much scientific research. The most recent articles published in the last three years are analyzed in this paper. Publications focused on the topic of reducing energy consumption in ventilation systems were selected and divided into five key research areas: (1) the aspect of the airtightness of buildings and its importance for the energy consumption, (2) the methods and effects of implementing the concept of demand-controlled ventilation in buildings with different functions, (3) the possibilities of the technical application of decentralized ventilation systems, (4) the use of earth-to-air heat exchangers, (5) the efficiency of exchangers in exhaust air heat-recovery systems. The multitude of innovative technologies and rapid technological advances are reflected in articles that appear constantly and prompt a constant updating of knowledge. This review constitutes a relevant contribution to recognizing current advancements in ventilation systems and may be helpful to many scientists in the field. [ABSTRACT FROM AUTHOR]

The rising cost and demand for energy have prompted the need to devise innovative methods for energy monitoring, control, and conservation. In addition, statistics show that 20% of energy losses are due to the mismanagement of energy. Therefore, the utilization of energy management can make a substantial contribution to reducing the unnecessary usage of energy consumption. In line with that, the intelligent control and optimization of energy management systems integrated with renewable energy resources and energy storage systems are required to increase building energy efficiency while considering the reduction in the cost of energy bills, dependability of the grid, and mitigating carbon emissions. Even though a variety of optimization and control tactics are being utilized to reduce energy consumption in buildings nowadays, several issues remain unsolved. Therefore, this paper presents a critical review of energy management in commercial buildings and a comparative discussion to improve building energy efficiency using both active and passive solutions, which could lead to net-zero energy buildings. This work also explores different optimum energy management controller objectives and constraints concerning user comfort, energy policy, data privacy, and security. In addition, the review depicts prospective future trends and issues for developing an effective building energy management system, which may play an unavoidable part in fulfilling the United Nations Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today's environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications. [ABSTRACT FROM AUTHOR]

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO2/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO2. The carbon emissions will peak at 5275.5 tCO2. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO2. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents' awareness of energy conservation. [ABSTRACT FROM AUTHOR]

Despite the considerable breakthrough in district heating systems (DHS) globally, there is not yet any policy on developing this technology in Iran. This country has a high range of energy demand, while renewable energies play a minor role in its energy supply chain. Furthermore, the world is going through a transition towards renewable resources, which currently consist of only 10% of the total energy mix. As the first contribution in this area, this paper aims to design a 100% renewable DHS integrated with radiant floor heating for a group of residential buildings in Sarein, Iran. Moreover, the literature proposes a novel approach for combining geothermal energy and Municipal Solid Waste (MSW) to achieve a 100% renewable energy system. Building Information Modeling (BIM) is used for thermal analysis by 3D designing the buildings in SketchUp and OpenStudio and simulating the heat load in EnergyPlus. Three scenarios are presented to better compare the DHS with the decentralized heating system regarding fuel consumption, as well as environmental and economic aspects. The town's existing heating system that consumes natural gas for both space heating and hot water demand is referred to as the IHS-G scenario. The DHS-G scenario represents an 87% renewable DHS system, working with natural gas and geothermal energy, while the DHS-MSW scenario is a 100% renewable system, consuming both geothermal energy and Municipal Solid Waste (MSW). Finally, findings suggest that DHS-MSW and DHS-G scenarios reduce the annual energy consumption of buildings by about 595 and 33 toes, respectively. Hence, the greenhouse gas effect will alleviate by mitigating the emission of 1403 and 1339 tons of CO2-eq./year, respectively. Moreover, exporting the extra natural gas through both LNG and pipeline provides about 26 million and 28 million USD/year revenue in DHS-G and DHS-MSW scenarios. [ABSTRACT FROM AUTHOR]