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Highlights • Definition of passive envelope indexes and caracterization of libraries in La Plata. • Environmental monitoring: achievement of preventive conservation indexes. • Correlation between passive envelope indexes and preventive conservation indexes. • Determination of the most influential passive envelope indexes for the achievement of preventive conservation indexes. Abstract The common approach for libraries to preserve their paper-based collections from irreversible deterioration is by controlling quantitative parameters, such as temperature and relative humidity. Variations of these parameters for preventive conservation can be expressed by the "performance index" (PI) and the "amplitude index" (AI). Building envelope is the first filter of the external climate, buffering the external weather fluctuations and maintaining the building's indoor climate stable. Its shape, window areas and envelope materials are the most influential parameters. As they capture the relationship of a few geometric and physical variables with the building's performance, the envelope characteristics can be expressed as certain passive envelope indexes. This paper studies the relationship of certain passive envelope indexes and the preventive conservation indexes for books for several libraries and archives located in La Plata, Argentina. The results show the impact of every passive envelope index upon the preventive conservation indexes. The compactness, heaviness, adjacency to spaces and adjacency to ground have a positive linear correlation meanwhile transparency and global transmittance are parameters with a negative correlation. In general, the most influential parameter is the relative area adjacent to other spaces, followed by global transmittance, transparency, heaviness, relative area adjacent to ground and compactness. [ABSTRACT FROM AUTHOR]

Much research has been focused on maintaining stable humidity conditions in buildings housing heritage collections while reducing energy use. Moisture buffering by collections themselves can have a marked effect on the stabilisation of relative humidity (RH), the key parameter for preservation. Modelling of moisture transport using COMSOL Multiphysics was applied to transform three-dimensional paper and wooden objects into their one-dimensional representations, without changing the moisture uptake and release characteristics. The results were coupled to the modelling of indoor microclimate and energy consumption in collection storage spaces with the use of WUFI ® Plus software. The study revealed the crucial impact of air exchange rate of the building on the stability of indoor RH and the humidification and dehumidification loads required to maintain it. In the adequately air-tight library store, a sizeable paper collection was found to reduce the RH fluctuations from ±9% to ±6% around the yearly average and the energy consumption due to the humidification and dehumidification load by 38% when compared with the empty space, for a high-quality climate control scenario. In turn, a wooden collection, occupying a realistic fraction of a museum store was not large enough to significantly narrow down the RH variations and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines. [ABSTRACT FROM AUTHOR]

Cooling system is in charge of a high percent of total energy consumption in buildings. Different cooling systems and devices have different effects on the cooling load and energy consumption. The main objective of this paper is to study the effects of floor and ceiling temperature in comparison with other parameters including location of air diffusers and application of insulation and sealant. On the other hand, hospitals are huge buildings with high energy requirements and systems compared to other buildings such as universities, schools and offices. In this paper the impacts of these different cooling parameters on the temperature and cooling load in a room of a hospital are studied using Design of Experiments (DOE) and the Taguchi method. The experiments are simulated in Energy Plus to achieve the comfort temperature in one hospital’s room in Iran during six months of spring and summer. Using different cooling systems such as chilled floor (during night to decrease the peak load during day) and ceiling in combination with air handling unit can provide people satisfaction while optimizing energy consumption. The results show that the location of inlet and outlet diffusers has the most effect while the chilled floor (when the other systems are off) has the less impact on reaching comfort temperature. The ranking of parameters and their interactions has been discussed through the paper. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the optimization capabilities of the salp swarm algorithm (SSA), a metaheuristic algorithm capable of addressing contemporary global challenges. The paper focuses on assessing SSA as an optimizer and observing its impact as a predictor in an example energy problem to gauge its predictive power. Salp swarm algorithm (SSA) distinguishes itself with its optimization capabilities, providing effective solutions to optimization problems. The quality, competitiveness, and efficiency of the algorithm were initially assessed using the CEC 2019 and CEC 2020 function sets. The results demonstrated that SSA is a competitive, effective, and up-to-date algorithm. This competitive nature suggests that SSA can be effectively employed across a wide range of problems. Therefore, the paper aims to evaluate its success in providing solutions to an energy prediction problem. In addressing the challenge of effective energy utilization, the accurate prediction of heat loading (HL) and cool loading (CL) factors, critical in building design, contributes significantly to the solution. In solving this problem, machine learning algorithms, specifically the multi-layer perceptron (MLP) as an artificial neural network architecture, were chosen. SSA was approached in a supervised manner, and a comparison with alternative metaheuristic algorithms was conducted. The obtained results indicate that the SSA-based MLP architecture (SSA-MLP) exhibits effective predictive capabilities in energy problems. By combining the optimization power of SSA and the learning capabilities of MLP, a robust solution with a competitive advantage in energy efficiency is presented. [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 Qinghai–Tibet plateau, with an average altitude of over 4000 m, has low annual average temperatures and a high demand for building heating. This region's abundant solar energy resources hold substantial practical significance for improving the indoor heat environment and reducing building energy consumption. This paper investigates the impact of orientation and skylight area ratio on building heat load and indoor temperature, using both actual measurement and simulation methods, with a case study of the comprehensive building at Beiluhe Observation and Research Station of Frozen Soil Engineering and Environment (Beiluhe Station), located in the Qinghai–Tibet Plateau region. Initially, a model was established using the EnergyPlus 9.4 software, with orientation variables set from east to west in 15° increments, to simulate the variations in building heat load resulting from orientation changes; simulations were then conducted for three different skylight area ratios under the optimal orientation to evaluate their influence on heat load and indoor temperature. The results show that for the architectural style examined in this paper, the optimal building orientation within the region is 30° south by east, with the optimal orientation range spanning from 45° south by east to due south. Heating load is negatively correlated with the skylight area ratio, and beyond a certain threshold, the rate of decrease in heat load diminishes or even stabilizes. The conclusions of this paper offer guidance for the orientation and skylight design of new buildings on the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Climate change has emerged as a global focal point, notably affecting vulnerable countries like Pakistan. The country faces heightened risk, prompting concern among many due to its susceptibility to climate-related hazards. An imperative challenge arising from this phenomenon is the necessary shift towards sustainable energy, particularly concerning energy consumption within the building sector. Buildings significantly contribute to daily energy usage and subsequent GHG emissions. In Pakistan, transitioning to energy-efficient building stock holds pivotal importance in mitigating climate change impacts. This hinges on establishing a robust institutional framework and regulatory structure centered around energy efficiency. While Pakistan possesses policies and national guidelines advocating for energy-efficient buildings, their practical enforcement and application remain inadequate. The inception of the Building Energy Code of Pakistan in 1990, based on ASHRAE 90.1 standards, marked a step forward. However, obstacles rooted in institutional weakness and limited capacity building have impeded its effective adoption and adherence. Although the Energy Provisions of 2011 introduced modifications, their scope remained confined mainly to commercial structures. This paper conducts an extensive assessment of Pakistan's existing institutional and regulatory framework for building energy efficiency and transition. Drawing on literature insights and stakeholder perspectives, an in-depth understanding of prevailing policies and institutions uncovers underlying reasons for noncompliance and limited implementation. Additionally, input was gathered from stakeholders through interviews, aiming to identify barriers obstructing the reinforcement of building energy efficiency measures in Pakistan. Discourse and narrative analysis, informed by literature and stakeholder interviews, revealed the significance of awareness, participation, coordination, capacity building, technical expertise, finance, and resources as key factors contributing to deficient compliance and implementation of existing codes and policy guidelines. This paper also proposes actionable recommendations to expedite the transition towards a sustainable, energy-efficient building stock in Pakistan. [ABSTRACT FROM AUTHOR]

To effectively reduce building energy consumption, a novel full fresh air system with a heat source tower (HST) and a borehole heat exchanger (BHE) was proposed for space cooling and dehumidification in this paper. The cooling system only adopts geothermal energy to produce dry and cold fresh air for space cooling and dehumidification through the BHE and HST, which has the advantage of non-condensate water compared to BHE systems integrated with a fan coil or chilled beam. Based on the established mathematical model of the cooling system, this paper analyzed the system characteristics, feasibility, operation strategy, energy performance, and cost-effectiveness of the proposed model in detail. The results show that the mathematical model has less than 10% error in estimating the system performance compared to the practical HST–BHE experimental set up. Under the specific boundary conditions, the cooling and dehumidification capacity of this system increases with the decrease in the air temperature, air moisture content, and inlet water temperature of the HST. The optimal cooling capacity and the system COP can be achieved when the air–water flow ratio is at 4:3. A case study was conducted in a residential building in Shenyang with an area of about 1800 m2. It was found that this system can fully meet the cooling and dehumidification demand in such a residential building. The operation strategy of the cooling system can be optimized by adjusting the air–water flow ratio from 4:3 to 3:2 during the early cooling season (7 June–1 July) and end cooling season (3 August–1 September). As a result, the average COP of the cooling system during the whole cooling season can be improved from 6.1 to 8.7. Compared with the air source heat pump (ASHP) and the ground source heat pump (GSHP) for space cooling, the proposed cooling system can achieve an energy saving rate of 123% and 26%, respectively. Considering that the BHE of the GSHP can be part of the proposed HST–BHE cooling system, the integration of the HST and GHSP for space cooling (and heating) is strongly recommended in actual applications. [ABSTRACT FROM AUTHOR]

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Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings. [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]

A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anomalies and their relationship in time. The outcomes of anomaly detection are one-sided. To address the above problems, this paper proposes an anomaly detection method based on multivariate time series. Firstly, in order to extract the correlation between different feature variables affecting energy consumption, this paper introduces a graph convolutional network to build an anomaly detection framework. Secondly, as different feature variables have different influences on each other, the framework is enhanced by a graph attention mechanism so that time series features with higher influence on energy consumption are given more attention weights, resulting in better anomaly detection of building energy consumption. Finally, the effectiveness of this paper's method and existing methods for detecting energy consumption anomalies in smart buildings are compared using standard data sets. The experimental results show that the model has better detection accuracy. [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]

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]

Approximately 40 % of the final energy is used for heating and cooling of buildings. Of this, as much as 75 % is used in residential buildings. The proportion of energy needed in buildings is relatively high, so it is necessary to start using technologies that have low energy consumption, use it efficiently or provide it from renewable sources. One such technology is thermal energy storage, which allows us to store excess energy for later when we need extra energy. The paper presents two experiments in the field of adsorption space heating. In the first experiment, measurements were carried out on the adsorbents - zeolite 13X and zeolite NaYBFK, which were placed into a duct, through which humid air was transported by means of a centrifugal fan. In the second experiment, water vapour as the working medium was used. With the first experiment we achieved better water uptake on NaYBF, while the second experiment shows the increase of water uptake of zeolite 13X. [ABSTRACT FROM AUTHOR]

With the ever-increasing population and historic highest energy demand, the energy efficiency of buildings is becoming crucial. Architectural firms are moving from traditional Computer-Aided Design (CAD) to BIM. However, nearly 40% of the energy consumption is due to buildings. Therefore, there is a need to integrate BIM with Building Energy Modeling (BEM), which presents an innovative opportunity to demonstrate the potential of BIM to minimize energy consumption by integrating building information software with data from existing energy-efficient building automation systems (EBAS). BEM is a form of computational analysis that can be used in conjunction with BIM or Computer-Aided Engineering (CAE) systems. In this paper, an attempt has been made to explore the existing literature on BIM and BEM and identify the effect of the integration of BEM in BIM in the design phase of the project. A recent survey from the last ten years (2012 to 2023) was carried out on Google Scholar, Web of Science, Science Direct, and Scopus databases. Inclusion/exclusion criteria were applied, and papers were scrutinized. From the results, it can be observed that the convergence of BIM and BEM is found to be useful in practical applications; however, projects with short life cycles might not be suitable for this solution. Challenges exist in the interoperability tools which have restrictions on data exchange. Binary translation is found to be the most suitable candidate for data exchange. The analysis further showed that the most used program for integrating BIM/BEM is Green Building Studio developed by Autodesk to improve construction and operational efficiencies. [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]

Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi'an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi'an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m2, and an LCCO2 of 72.3 kgCO2/m2. The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m2, and 0 kgCO2/m2. This reflects the potential of solar energy utilization to reduce buildings' energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China. [ABSTRACT FROM AUTHOR]

With the background of rural revitalization, the urgent demand for energy conservation and improved living quality arises alongside the issues of high energy consumption and low comfort in residential buildings. Located in a region with a hot summer and cold winter climate, Hanzhong faces significant energy consumption for heating and cooling throughout the year, considering both winter insulation and summer heat insulation. Based on the energy consumption simulation and analysis of folk dwellings in Hanzhong, this paper employs a single-objective optimization method to explore the optimization of building envelope structures, including the window-to-wall ratio, bay width, number of floors, orientation, and floor height. Additionally, it investigates building layout, spatial organization, regional design methods, and energy acquisition. Through energy consumption simulation and validation of thermal comfort evaluation index PMV-PPD, design strategies such as building scale, layout organization, indoor and outdoor buffer space design, and building material selection are proposed to effectively improve indoor thermal comfort during the winter and summer seasons. This research provides insights and references for the low-carbon design and optimization of residential buildings. [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]

External walls, serving as the primary medium for heat exchange between the building and the external environment, has its thermal loss comprising the largest proportion of building energy consumption. Therefore, enhancing the thermal insulation capacity of the wall is of great significance in reducing building energy consumption. In this paper, a novel sustainable prefabricated expanded polystyrene (EPS) thermal insulation wall panel with irregular column frame structures was developed. And weathering tests combined with finite element simulations were conducted to investigate its weathering performance and degradation patterns. The results revealed that In the weathering test, the panel surfaces did not exhibit apparent water seepage cracks, powdering, hollowing, peeling, etc. There was no occurrence of facing brick detachment or damage. The outer surface concrete of the wall panel experienced resistance during normal thermal expansion and contraction, generating compressive stress during expansion and tensile stresswhen contracted.In addition, the bond strengthof the specimens decreased by 8.1% after the thermal-rain cycles, 5.1% after the thermalcold cycles, and 12.1% after the freeze-thaw cycles. In the numerical simulations, the temperature stress at various positions on the concrete wall had a noticeable mutual restraining effect on the force deformation of the nearby concrete. There was a significant risk of cracking in themiddle and around the opening, particularly in the lower part of the wall panel. This study serves as a basis for the degradation analyses and optimization design of the sandwich insulation wall panels for sustainability. [ABSTRACT FROM AUTHOR]

The building sector is responsible for nearly 40% of the total global direct and indirect CO2 emissions. Urban green infrastructure, which includes features such as urban trees, vegetation, green roofs, and green facades, are examples of nature-based solutions often employed as municipal climate mitigation and adaptation strategies. This approach offers a range of cost-effective strategies for reducing municipal CO2 emissions and presents compelling public policy co-benefits such as improved urban livability and enhanced environmental conditions. For municipalities to confidently deploy these solutions at a scale necessary to achieve climate benefits, acquiring knowledge of quantifiable and demonstrated outcomes is an essential requirement. The objectives of this paper are to (1) provide a comprehensive analysis of the advantages and limitations of nature-based solutions (NBS) to address the challenge of reducing CO2 emissions; (2) evaluate existing design guidelines and policies as may be available across Canada, and that that support the of implementation of NBS in urban agglomerations; (3) identify knowledge gaps and research needs to address challenges to the implementation of NBS. In this review, suggestions and requirements as presented in these documents are examined while giving due consideration to the scientific evidence available in research papers. It was found that the adoption of NBS can contribute to carbon neutral communities through reduced building energy consumption and carbon sequestration. Supportive guidelines and policies have been developed, or are in development, to promote the implementation of NBS at the city scale, despite challenges in assessing, quantitatively, their impact due to uncertainties in data, methods, and scale. Nonetheless, existing research provides sufficient evidence to qualify the measures and suggestions outlined in the guidelines and policies described in this paper. [ABSTRACT FROM AUTHOR]

The sun's total radiation alone exceeds the world population's entire energy consumption by 7.500 times and ignites secondary renewable energy sources. The end energy consumption buildings use for heating amounts to 28% of Germany's total energy consumption. With the ongoing trend of digitalization and the transition of the German energy supply away from fossil fuels and the consequent political dependency, electric heat pumps and photovoltaic (PV) systems have become increasingly important to the discussion. This has led to an increasing demand for smart control strategies, especially for inert systems such as thermally activated building systems (TABS). This paper presents and analyses a weather predictive control (WPC) strategy using a validated thermodynamic simulation model. The literature review of this paper outlines that the current common control strategies are data intense and complex in their implementation into the built environment. The simple approach of the WPC uses future ambient temperature and solar radiation to optimize the control of the heating, cooling, ventilation, and sun protection system. The thermal comfort and energy demand evaluate the concept. We show that with a WPC for TABS, thermal comfort can improve without increasing the energy demand for the office building in the moderate climate of Munich. Furthermore, this paper concludes that the WPC works more effectively with more thermal mass. This simplified building control strategy promotes the European roadmap goal of climate neutrality in 2050, as it bridges the phenomenon of the performance gap. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

Earth–air heat exchangers (EAHX) use the soil thermal capacity to dampen the amplitude of outdoor air temperature oscillations. This effect can be used in hot and dry climates for room cooling, and depending on the EAHX design, this cooling can be achieved with very few resources other than those used during EAHX construction. This is an obvious advantage compared to the significant energy consumption and operational costs of refrigeration machines traditionally used in room cooling. Despite the large number of papers on EAHXs available in the scientific literature, very few deal with large-diameter EAHXs (with pipe diameters larger than 0.30 m), and even fewer present monitoring data gathered from a built and functional large-diameter EAHX. The present paper uses monitoring data and provides a detailed quantitative analysis of the performance of a large-diameter EAHX built for standalone cooling of an existing office building. The field monitoring was carried out during a characteristic hot and dry summer period of the south of Portugal. Results show that outdoor air to EAHX exit air temperature gradients reach 9 K and cooling capacities exceed 27 kW. Moreover, the studied EAHX is capable of standalone cooling for outdoor air temperatures up to 33 °C and meets more than 50% of the room design cooling demand for outdoor air temperatures as high as 37 °C. This evidences that large-diameter EAHXs have the potential to achieve significant reductions in CO 2 emissions and in energy consumption associated with building cooling in hot and dry climates. [ABSTRACT FROM AUTHOR]

To realize carbon neutrality, understanding the energy consumed in the building sector, which is more than that in other sectors, such as industry, agriculture, and commerce, is pivotal. Approximately 37% of energy consumption belongs to the building sector, and management of building energy is a critical factor. In this paper, we present an energy sharing scenario for energy stabilization, assuming that electric vehicles and their charging stations are widely distributed in the future. Consequently, fewer fuel cars will exist, and electric cars will become the major mode of transportation. Therefore, it is essential to install charging stations for electric vehicles in the parking lots of future buildings, and business models are expected to expand. In this paper, we introduce a future energy stabilization mechanism for peak power management in buildings and present a platform that entails connection-based energy trading technology based on a scenario. We also propose an energy supply strategy to prevent excess prices incurred due to peak consumption. Then, we analyzed the electricity bill for one month through scenario-based simulations of an existing building and the proposed system. When applying the proposed system, we derived a result that can reduce electricity rates by 38.3% (best case) to 78.5% (worst case) compared with the existing rates. [ABSTRACT FROM AUTHOR]

Achieving zero energy consumption in buildings is one of the most effective ways of achieving 'carbon neutrality' and contributing to a green and sustainable global development. Currently, BIPV systems are one of the main approaches to achieving zero energy in buildings in many countries. This paper presents the evolution of BIPV systems and predicts their future trends by deriving a base sample of core papers on BIPV systems from 2012 to 2022 from the Web of Science core database and conducting a bibliometric study using CiteSpace scientific visualisation software. To gain a deeper understanding and grasp of the research progress of BIPV systems, research group discovery, research hotspot analysis, and research frontier detection of the relevant literature were conducted. (1) Research groups on the topic were summarised through author coupling network, publication distribution, and country mapping analysis; (2) Research hotspots on the topic were explored through keyword co-occurrence, keyword emergence, and time zone map analysis; (3) Research hotspots on the topic were explored through literature co-citation timeline maps, literature co-citation categories, and literature co-citation clustering analysis to detect the frontiers of research in the field. Finally, we conclude that research trends in BIPV systems are mainly in the areas of heat transfer, thermal performance, renewable energy, solar cell and renewable building materials, and evaluation systems. In the future, BIPV research and applications will move towards interdisciplinary and multinational cooperation, which will maximise the benefits of clean energy conversion in buildings. It will also provide researchers and practitioners with a clearer understanding of BIPV research trends and hotspots, and provide new directions for future research. [ABSTRACT FROM AUTHOR]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in building performance simulation software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. The findings indicate that by analyzing measured data and adjusting the configurable parameters, the energy consumption of buildings can be significantly reduced. The close monitoring of indoor parameters by building operators and making corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, this paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives. [ABSTRACT FROM AUTHOR]

With the trend of achieving both energy efficiency in buildings and occupants' comfort, gamification strategies have started to be developed and applied as incentive mechanisms to increase social interaction and facilitate human energy behavior transformation. In this article, 306 published papers are reviewed, and 21 studies are identified to determine the challenges and potential for the development of gamification strategies to improve building energy efficiency. Specifically, this work reviews the implementation techniques of gamification and methods to assess the impact of gamification mechanisms on human energy behavior changes. This analysis demonstrates that, firstly, the choice of an optimal gamification implementation method should be inherently attuned to the distinct characteristics of the building type and its occupants. Secondly, it is imperative to strike a judicious balance between extrinsic and intrinsic motivations, in which customization of gamification design elements are based on users' unique personality traits and preferences, to properly tailor gamification mechanisms. Thirdly, integrating a fusion of quantification of energy savings and qualitative interpretation of user behaviors to improve the energy efficiency in buildings is essential for a more holistic understanding of the impact of gamification on users' energy-related behavior change. The findings indicate that gamification techniques can enable the effective reduction of energy consumption in buildings. [ABSTRACT FROM AUTHOR]

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA- MLPRegressor performed well in terms of different metrices variables and fitted time. [ABSTRACT FROM AUTHOR]

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

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [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]

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

Kindergartens are probably the very first public buildings we actively use in our lives. Therefore, they can be considered as the physical structures that are providing for the very important educational and social function. Additionally, they can also be considered as the specific learning tool for future generations where they can encounter the ideas of green and energy-efficient buildings. One of the results of the research project "Energy efficiency in public buildings" has been formulated through the development of Serbian National Typology of Kindergartens which was conceived as a specific tool that can be used for improving this portion of building stock. The paper presents the methodology for identification of typical kindergarten buildings, covering various construction periods, building sizes and illustrates the type of analysis performed for model representatives. The potential for energy upgrades covering physical structure, installed technological systems, as well as expected impact on energy performance, has been estimated. The study presents results based on the analysis of the data derived from the National Typology, stressing out that energy retrofits of large and medium-sized kindergartens built during the 1970s and 1980s should be the primary focus of refurbishment activities. Accounting for almost 60% of total buildings and 69.59% of heated area, they are responsible for 75.97% of energy demands. Estimated energy savings of more than 60% indicate the effectiveness of their retrofit. The paper also presents the potential uses of National Typology as a retrofit tool on various scales, from single-building considerations to a strategic approach at the national level. [ABSTRACT FROM AUTHOR]

For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate. [ABSTRACT FROM AUTHOR]

This paper uses SketchUp and OpenStudio to design and optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. By establishing a basic model, the performance parameters such as insulation thickness, typical outer window, window-to-wall ratio, and their contribution rates within the threshold range are optimized for energy conservation; simulate the energy consumption of the determined multiperformance parameter combination scheme, select the most energy-saving scheme, and calculate its energy-saving contribution rate; calculate the life cycle cost of the above schemes, select the scheme with the lowest cost, and analyze its economic benefits; select energy-saving cost and economic benefit, and calculate energy-saving contribution rate and economic benefit. Finally, through the comparative analysis of the design and test data, this paper concludes that EnergyPlus will optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. At the same time, the multiobjective optimization method and process of the passive energy-saving technology of the near-zero energy consumption building proposed in this paper, as well as the technology combination template under different needs, provide a reference for the selection of the technology path. [ABSTRACT FROM AUTHOR]

The scarcity of energy is one of humanity's most pressing issues in the twenty-first century. These problems can be found in various areas, including energy supply, exchange, and consumption. Population growth, rising global energy demand, natural resource shortages, and environmental concerns contribute to energy scarcity. Furthermore, energy shortage needs the expansion of renewable energies and energy efficiency, a major concern for all governments and organisations. Building energy efficiency is a crucial concept to consider when it comes to smart cities. Buildings are the greatest energy consumers, accounting for 40% of total energy consumption. Recent advances in machine learning within a big data context have paved the way for more efficient building energy management. Building energy consumption may be successfully managed by a real-time measurement procedure, allowing the economy to shift from a linear to a circular consumption model. This will tackle the issue of late notice of failed energy-saving initiatives and allow for immediate correction to restore the energy management system to ultimate performance.The energy hub might be as small as a single home energy system or as large as an energy system for the city. This paper will present a case study on developing a smart energy hub called Hubgrade 4.0 that relies on connected products using digital twin as a significant enabler for energy-saving initiatives. Hubgrade 4.0 provides an innovative approach to successfully implementing energy efficiency improvement using artificial intelligence and real-time data. Hubgrade 4.0 is the name of Enova by Veolia smart energy hub; Enova is the regional leader in integrated energy and multi-technical services, delivering performance-based energy and facilities management solutions. The energy hub digital twin is a link between a physical platform that administers the energy hub's IoT and a virtual platform that can derive services that are valid for the energy hub. Successful enterprises are using several new technologies to achieve the goals of Industry 4.0: efficiency, speed, agility, and customer-centricity. This paper concluded and highlighted lessons learned from the successful implementation of innovative energy management, which relied on a dedicated organisation, effective adoption of digital technologies, and embracing new business models, resulting in power savings of 254 million kW and water savings of around 3 million cubic meters, as well as financial savings of about 138 million AED in 5 years since Hubgrade 4.0 started operations its first energy-saving contract in 2017. [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]

Climate change and rapid urbanization increase/amplify urban heat islands (UHIs). Green infrastructure (GI) is an effective and popularly strategy used to moderate UHIs. This paper aims to better understand the progress of different GI types (urban parks, urban forests, street trees, green roofs, green walls) in mitigating UHIs, and what benefits they provide. Firstly, this paper used CiteSpace to analyze 1243 publications on the Web of Science from 1990 to 2021, then analyzed the function/regulation of ecosystem services/benefits and values of GI types in reducing UHIs. The historical review results show that research on all GI types showed rapid growth since 2013, and their GR increased rapidly. The highest-ranking keywords were urban heat island/heat island, climate/climate change/microclimate, and temperature/land surface temperature/air temperature. "Design," "vegetation," "quality," and "reduction" are the top four strongest keyword bursts. The most published countries are the People's Republic of China, USA, Australia, Germany, and Italy, and the top three institutions are the Chinese Academy of Sciences, Arizona State University, and the National University of Singapore. Landscape and Urban Planning, Building and Environment, Energy and Building, and Urban Forestry and Urban Greening are the most published journals. In urban areas, different GI types as a form of ecosystem hardware provide multiple functions (reduced land surface temperatures, lower building energy usage, improved thermal comfort and enhanced human health, reduced morbidity and mortality, etc.). GI thus provides a regulated ecosystem service to ameliorate UHIs primarily through temperature regulation and shade. At the same time, GI provides benefits and values (ecological, economic, social, and cultural) to humans and urban sustainable development. GI types determine the functions they provide, afford corresponding regulated ecosystem services, and provide benefits and values in a logical/recycle system. Overall, this review highlights the development and importance of GI, as well as the relationship of GI types and functions of regulating the ecosystem service benefits and values to mitigate UHI, and advances the study of climate change adaptation in cities. [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]

Forecasting energy consumption models allow for improvements in building performance and reduce energy consumption. Energy efficiency has become a pressing concern in recent years due to the increasing energy demand and concerns over climate change. This paper addresses the energy consumption forecast as a crucial ingredient in the technology to optimize building system operations and identifies energy efficiency upgrades. The work proposes a modified multi-head transformer model focused on multi-variable time series through a learnable weighting feature attention matrix to combine all input variables and forecast building energy consumption properly. The proposed multivariate transformer-based model is compared with two other recurrent neural network models, showing a robust performance while exhibiting a lower mean absolute percentage error. Overall, this paper highlights the superior performance of the modified transformer-based model for the energy consumption forecast in a multivariate step, allowing it to be incorporated in future forecasting tasks, allowing for the tracing of future energy consumption scenarios according to the current building usage, playing a significant role in creating a more sustainable and energy-efficient building usage. [ABSTRACT FROM AUTHOR]

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours. [ABSTRACT FROM AUTHOR]

With the improvement of the living standards of the Chinese people, the construction of the economic system will change from one that meets the needs of the people to one that is perfect. Under the current environment of building a resource-saving socialist harmonious society, it is very necessary for architectural design practitioners to return to the original architectural design and pay attention to the design and use of building materials during construction. The purpose of this paper is to explore and analyze the application of energy saving and environmentally friendly materials in architectural design. This paper firstly analyzes the building's energy consumption and building's energy efficiency. Every major achievement in building energy efficiency will be an important means of mitigating the Earth's energy. Building energy saving has become a major trend of global development, and then the optimal design of reinforcement selection for reinforced concrete beams based on a multiobjective genetic algorithm is discussed. The area of reinforcement required for the member control section is obtained according to the structural analysis program, the main purpose of reinforcement selection is to select safe, economical, and reasonable reinforcement on the premise of meeting the provisions of the code and the requirements of reinforcement structure. Finally, this paper conducts experimental research on the application of phase change energy storage building material SP28 in building energy conservation. The experimental results in this paper show that when the mass ratio of the phase change energy storage material in the wall is less than 15%, there is no wetting and leakage phenomenon. However, when the mass ratio of the phase change energy storage material contained in the wall is greater than 15%, the phenomenon of wetting and leakage will occur, which will cause greater losses when added to the wall. Therefore, it is not suitable for application and building walls. Regardless of leakage, wetting, or economic considerations, the mass ratio of phase change material of 10% is the most suitable for use in walls. It not only has no wetting and no leakage but also has a good energy storage effect and can effectively adjust the ambient temperature. [ABSTRACT FROM AUTHOR]