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The global drive for sustainable development and carbon neutrality has heightened the need for energy-efficient buildings. Photovoltaic buildings, which aim to reduce energy consumption and carbon emissions, play a crucial role in this effort. However, the potential of the building envelope for electricity generation is often underutilized. This study introduces an efficient hybrid method that integrates Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. This integrated approach was used to optimize the external envelope structure and photovoltaic components, leading to significant reductions: overall energy consumption decreased by 41% (from 105 kWh/m2 to 63 kWh/m2), carbon emissions by 34% (from 13,307 tCO2eq to 8817 tCO2eq), and retrofit and operating costs by 20% (from CNY 13.12 million to CNY 10.53 million) over a 25-year period. Sensitivity analysis further revealed that the window-to-wall ratio and photovoltaic windows play crucial roles in these outcomes, highlighting their potential to enhance building energy performance. These results confirm the feasibility of achieving substantial energy savings and emission reductions through this optimized design approach. [ABSTRACT FROM AUTHOR]

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

Due to its significance and effect on interior spaces, photovoltaic Trombe walls are regarded as sustainable technologies. This is because they use solar energy to heat the building and give thermic comfort without requiring the use of cooling equipment to reduce power consumption. not to mention supplying the structure with electricity. To investigate how operational conditions and climatic change affect PV/TW, two empirical models--one with DC fans and the other without--were developed. The two models were compared with each other. To demonstrate their impact on system competency, operating conditions for both dusty and non-dusty days have been examined. When DC fans are used on a dust-free day, the system's electrical and thermal competence is 10.2 percent and 17.6 percent, respectively. The system's electrical and thermal competence levels were 8.4 percent and 40.1 percent, respectively, on a day when there was no dust and when the DC fans were not operating. The system that had fans had a higher thermic and electrical efficiency than the other two systems, with values of 11.9 percent and 6.6 percent, respectively, on the dusty days. The system's thermic and electrical efficiency were 34.1 percent and 3.5 percent, respectively, without fans. This suggests that dusty days have an impact on the experimental system's electrical and thermic efficiency. [ABSTRACT FROM AUTHOR]

In urban building management, accurate prediction of building energy consumption is significant in realizing energy conservation and improving energy efficiency. Due to the complexity and variability of energy consumption data, existing prediction models face the challenge of difficult parameter selection, which directly affects their accuracy and application. To solve this problem, this study proposes an improved particle swarm algorithm (IPSO) for optimizing the parameters of the least squares support vector machine (LSSVM) and constructing an energy consumption prediction model based on IPSO-LSSVM. The model fully combines the advantages of LSSVM in terms of nonlinear fitting and generalization ability and uses the IPSO algorithm to adjust the parameters precisely. By analyzing the sample data characteristics and validating them on two different types of building energy consumption datasets, the results of the study show that, compared with traditional baseline models such as back-propagation neural networks (BP) and support vector regression (SVR), the model proposed in this study is more accurate and efficient in parameter selection and significantly reduces the prediction error rate. This improved approach not only improves the accuracy of building energy consumption prediction but also enhances the robustness and adaptability of the model, which provides reliable methodological support for the development of more effective energy-saving strategies and optimization of energy use to achieve the goal of energy-saving and consumption reduction and provides a new solution for the future management of building energy consumption. [ABSTRACT FROM AUTHOR]

Examining the thermodynamics of phase change materials (PCMs) when merged into construction materials is a significant subject within the realm of building science and environmental responsibility. When infused to construction materials like bricks, PCMs have the capacity to elevate a building's temperature regulation by minimizing the energy required for thermal contentment. This research is dedicated to learn about the thermal conduct and the consequences of the fusion of calcium chloride hexahydrate mineral (CCHPCM) within the pores of a masonry unit. To achieve this, we implemented a practical testing specifically designed to scrutinize how CCHPCMs alter the thermal performance of studied compounds. Multiple configurations were designed by adjusting the arrangement of CCHPCM within the bricks, resulting in three distinct setups. The first set involved filling one row of the bricks, the second set entailed filling two rows, and the final configuration entailed filling all the pores with PCM. Additionally, a computational modeling was executed to survey the thermic behavior of bricks infused with CCHPCM, operating with COMSOL Multiphysics application program. The elaborated work concluded to having an enhancement of the brick's thermal storage capacity, for Set-3, in which all rows of bricks are filled with PCM, a delay of 2 h is observed compared to Set-0 the brick without CCHPCM. This simulation also encompassed comparative findings regarding the thermal performance of CCHPCM when incorporated into the masonry unit. Overall, this study supplied the valorization of CCHPCMs infused in masonry units and their usage in distinct layouts on upgrading its candidature to achieving environmental responsibility. [ABSTRACT FROM AUTHOR]

The cooling and heating sector is responsible for the highest energy consumption in the building sector, comprising approximately 30% of the total. Extensive research has been conducted to address this issue and minimize energy consumption through the implementation of innovative technologies. Among these technologies, the passive earth‐air heat exchanger (EAHE) has proven highly effective in reducing energy usage in the cooling and heating sector. This research focused on optimizing U‐shaped EAHE systems and examined their functional and thermal‐fluidic parameters through numerical analysis. The simulation employed COMSOL Multiphysics software, and the results obtained were in excellent agreement with experimental data. The study investigated a base case, as well as five optimized cases with varying inlet velocities, to evaluate performance. The findings revealed that increasing the working fluid's inlet velocity led to a decrease in the system's thermal efficiency. However, at higher velocities, the economic parameters for energy production showed improvements. Specifically, the system generated a maximum energy output of 9132 W in the fifth case, operating at a velocity of 2 m/s. Additionally, the system achieved an impressive performance coefficient of approximately 5.13 in the same case, with an inlet velocity of 0.46 m/s. Notably, the lowest recorded output temperature of the system was 22°C at the specified inlet velocity. [ABSTRACT FROM AUTHOR]

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

Traditional concrete buildings exhibit low energy consumption and high heat loss, which results in a larger environmental problem. Precast insulation walls are proposed for strengthening thermal insulation efficiency and mitigating heat loss. Numerous studies have investigated the thermal performance of insulation walls over the past decades. However, gaps remain in practical engineering applications. This study aims to bridge these gaps by providing practical design recommendations based on experimental research. Nine different types of precast insulation walls were tested to examine the thermal performance, and the parameters of the insulation material, insulation form, insulation layer thickness, and concrete rib width were investigated. Then, numerical models of these walls were developed for simulating the thermal performance of the tested specimens. Finally, a six-story student apartment model using designed walls was developed to assess energy consumption in two distinct climate zones: the hot summer and cold winter zone of Changsha City, and the cold zone of Harbin City. The results indicate that the precast insulation wall with external insulation form shows better thermal performance than the sandwich insulation form. It is recommended to use precast insulation walls with 50 mm extruded polystyrene (XPS) external thermal insulation form in Changsha City and 80 mm XPS external thermal insulation form in Harbin City. Furthermore, buildings using precast insulation walls can significantly reduce energy consumption by 49.25% in Changsha and 49.38% in Harbin compared to traditional concrete wall buildings. Based on these findings, suitable design suggestions for this precast concrete wall panel building composed of insulation walls are given. [ABSTRACT FROM AUTHOR]

Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance. [ABSTRACT FROM AUTHOR]

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

Abstract With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

Abstract This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer.

Abstract The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model’s ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building's operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation. [ABSTRACT FROM AUTHOR]

This research investigates building energy consumption in the Fujian region of China, characterized by warm winters and hot summers. The study focuses on window configurations and their impact on heat exchange and solar gain management. Initially examining three aluminum alloy window frames, the study utilizes the Multi-Quality Metric Calculator (MQMC) software V1 to assess the benefits of filled insulating glass. The reference values for the heat transfer coefficient, visible transmittance, and sun shading coefficient are established. Subsequently, Ecotect software V5.6 is employed to conduct a comprehensive year-round energy consumption simulation analysis, identifying an optimal window layout tailored to Fujian's climate. In the Fuzhou simulation, aluminum–plastic co-extruded windows exhibit the lowest cooling energy consumption, while aluminum alloy windows have the highest. Summer cooling energy consumption, comprising about 75% of the total annual energy usage in hot summer and warm winter regions, significantly influences overall energy consumption. Windows made of aluminum–plastic co-extruded material with superior thermal insulation qualities can greatly reduce building energy consumption. The results contribute valuable insights to sustainable building practices and energy-conscious designs in regions characterized by warm winters and hot summers. [ABSTRACT FROM AUTHOR]

Building design optimization (BDO) provides an approach for decreasing global energy consumption and achieving the goal of carbon neutrality. However, energy efficiency and comfort performance are two conflicting objectives when making optimal building design schemes. This study proposes a surrogate model-based multiple-objective optimization framework to balance the conflicting objectives and obtain an optimal design scheme for buildings. Firstly, an energy simulation model for generating energy consumption and design parameters is constructed, and the obtained data are utilized to train the surrogate model with the random forest (RF) algorithm. Then, multi-objective optimization algorithms are employed to generate a set of alternative plans for building schemes and determine the optimal building design solutions that can equilibrate the requirements for both energy conservation and building comfort. To verify the proposed optimization method in this paper, a residential building in Suzhou was selected as a case study. The study considered 10 building design parameters that are related to energy efficiency and thermal comfort. The results indicate that the RF surrogate model accurately predicts energy consumption, with a predicted MSE of 0.00012 and R2 of 0.99. In evaluating the Pareto set size, Pareto solution diversity, Pareto front proximity, and best solution quality, NSGA-II proved to be the most effective optimization algorithm for BDO problems. The final optimal solution of design parameters obtained by NSGA-II obviously improves the building performance of comfort and energy efficiency, and the results of the performance evaluation for different optimization algorithms provide guidance to make decisions on suitable algorithms and hyperparameter settings based on the greatest preference of the performance criteria. This study will help determine the best design options for buildings to achieve better energy efficiency in sustainable development and provide reference for similar projects. Practical applications: This research makes valuable contributions in the following aspects:(a) It establishes a multi-objective optimization design model for a virtual building environment. This enables the visualization and digitization of the building model and further facilitates the transformation of the optimization model, thereby providing users with convenient decision-making tools; (b) The study provides designers and other stakeholders with comprehensive simulation-based analysis results and optimization techniques. These tools aid in making energy-saving multi-objective optimization decisions;(c) The research compares various optimization algorithms and presents their strengths and limitations, which will help designers select suitable algorithms based on practical requirements. [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]

The significant share of buildings from total energy consumption across the world has been mentioned and emphasized very well by several scholars. In this regard, there have been major developments and improvements in the expertise of developing and designing buildings to be adequately energy efficient. However, the recent studies show that there is still a considerable deviation between the intended and actual energy consumption of the completed buildings. Hence, this exploratory study aims to discover the origin of success and failure in achieving energy efficiency in building construction projects with a life cycle perspective and based on viewpoints of key participants in the project and constructed building's operation. To do so, 21 semi-structured interviews were conducted with Finnish project professionals representing client, design/planning experts, contractors and building operation/maintenance experts. Both thematic analysis and content analysis methods were employed for analysing the obtained research data. The findings reveal a set of challenges/barriers and solutions/enablers which account for failure and success of achieving energy-efficient buildings. The obtained results contribute to the existing body of knowledge and practices on achieving energy efficiency in building construction projects. [ABSTRACT FROM AUTHOR]

This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer. [ABSTRACT FROM AUTHOR]

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

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

It is necessary to reduce the huge energy consumption of buildings while ensuring indoor thermal comfort. The building envelope design notably affects the energy consumption and indoor thermal comfort. This study proposes a framework of support vector regression non-dominated sorting genetic algorithm-II (SVR-NSGA-II) to study the multi-objective optimization of buildings and discover the best building envelope design. First, the data are obtained by OpenStudio 1.1.0 simulation. Then SVR is used to describe the relationships between the parameters and the two objectives. Using SVR models as the objective functions, NSGA-II is applied to optimize the objectives. Finally, taking a proposed office building as an example, the developed framework is applied, verifying its feasibility and effectiveness. The optimized parameters of the building envelope are as follows: the exterior wall U-value, roof U-value, exterior wall U-value, solar heat gain coefficient value (SHGC), and south, north, east, west window-to-wall ratios are respectively equal to 0.664 W/(m2⋅K), 0.393 W/(m2⋅K), 1 W/(m2⋅K), 0.381, 0.697, 0.7, 0.273, and 0.254. The design of the proposed building can be modified to provide a more energy efficient and comfortable building. The proposed model saves time and simplifies operation compared with traditional building information modeling methods.

Previous optimization approaches with regard to the overall urban form can no longer meet the demand for accurately implementing building energy consumption carbon emission reduction at small spatial scales. This study constructed building energy consumption-oriented urban functional area division method using points of point-of-interest (POI), three-dimensional building and roadway data with urban functional areas as units. The method linked POI to buildings, using building volume as an auxiliary parameter to determine the functional attributes of blocks while taking into account the building operational energy consumption. The residential functional areas, commercial functional areas and industrial functional areas, which account for more than 90% of the total building operation energy consumption and the top three in terms of share, are selected as the analysis objects. Geographic detector was applied to analyze the mechanism of carbon metabolism in the compact spatial patterns of these three functional areas. The case study in Xiamen, China reveals that (1) Three-dimensional building compactness is an important factor influencing building energy consumption(normalized difference vegetation index q-value is 0.227); (2) the difference in energy consumption between different compactness classes in residential and commercial functional areas is significant, while not in industrial functional areas; (3) according to the three-dimensional building compactness optimization path of this study, the building operation energy consumption in central Xiamen City could be optimized to reduce by 7% in 2015. Based on the self-created building energy consumption-oriented functional area division method, it is concluded at the functional areas scale that different types of urban functional zones have different optimization methods for three-dimensional building compactness. Such optimization can save construction resources and achieve double carbon more effectively.

Opaque surfaces, such as walls, are well-known for their significant contributions to heat loss and energy demands in buildings. However, transparent surfaces, such as windows, are equally critical to a building's energy performance. The design of these transparent elements requires a careful balance of various factors, including window size, glazing type, and orientation, each of which plays a pivotal role in enhancing energy efficiency. This study explores the optimization of these factors during the design process, emphasizing their impact on the overall building performance.This research evaluates the potential energy savings in a building archetype representative of the Algerian building stock. Utilizing the EnergyPlus simulation tool, the study conducted 1152 simulations on a baseline model to generate a comprehensive dataset detailing the building's energy demands for heating and cooling across various climatic conditions. The findings reveal that annual energy savings for this type of housing essentially depend on its climatic zone and can range from 6.92 % for a hot semi-arid climate (Bsh) to reach a maximum of 9.75 % in a cold semi-arid climate (Bsk), a window-to-wall ratio (WWR) of 60 % typically maximizes energy efficiency, low-E glazing proved most effective in most cases, although regions needing significant solar protection favored alternative glazing types. Optimal window orientation generally trends Eastward, except in regions where southern exposure better supports solar management, highlighting the complex relationship between architectural design choices and energy efficiency.

The growing energy demand, associated with the inability of the current infrastructure to satisfy this demand, has presented numerous challenges in Iraq's electricity sector. As a result, there has been an increased dependence on local diesel generators to mitigate power outages in homes. However, these generators raise environmental concerns and are associated with high operating CO₂ emissions. Here, using the DesignBuilder and EnergyPlus simulation software, the effectiveness of different building envelope modifications and photovoltaic panels as alternative energy sources was examined. Specifically, the impact of wall and roof insulation, window glazing, and shading devices on energy efficiency was analyzed. The results indicated that roof insulation is the most effective in reducing energy consumption by 28.8%, followed by wall insulation by 13.01%, while the effect of windows glazing and shading devices was insignificant. Furthermore, the installation of solar panels led to a significant reduction in energy demand by 53.6%, thereby decreasing operating carbon dioxide emissions and providing a practical alternative to the use of local generators. Our study offers valuable insights into the design of energy-efficient residential buildings in hot and dry climates. It highlights the importance of selecting appropriate building materials and integrating renewable energy sources, presenting a more environmentally effective solution to mitigate energy shortages.

Accurate and effective building energy consumption prediction is an important basis for carrying out energy-saving evaluation and the main basis for building energy-saving optimization design. However, due to the influence of environmental and human factors, energy consumption prediction is often inaccurate. Therefore, this paper presents a building energy consumption prediction model based on an attention mechanism, time convolutional neural (TCN) network fusion, and a bidirectional gated cycle unit (BIGRU). First, t-distributed stochastic neighbor embedding (T-SNE) was used to preprocess the data and extract the key features, and then a BIGRU was employed to acquire past and future data while capturing immediate connections. Then, to catch the long-term dependence, the dataset was partitioned into the TCN network, and the extended sequence was transformed into several short sequences. Consequently, the gradient explosion or vanishing problem is mitigated when the BIGRU handles lengthy sequences while reducing the spatial complexity. Second, the self-attention mechanism was introduced to enhance the model's capability to address data periodicity. The proposed model is superior to the other four models in accuracy, with an mean absolute error of 0.023, an mean-square error of 0.029, and an coefficient of determination of 0.979. Experimental results indicate that T-SNE can significantly improve the model performance, and the accuracy of predictions can be improved by the attention mechanism and the TCN network.

Abstract Cooling load estimation is crucial for energy conservation in cooling systems, with applications like advanced air-conditioning control and chiller optimization. Traditional methods include energy simulation and regression analysis, but artificial intelligence outperforms them. Artificial intelligence models autonomously capture complex patterns, adapt, and scale with more data. They excel at predicting cooling loads influenced by various factors, like weather, building materials, and occupancy, leading to dynamic, responsive predictions and energy optimization. Traditional methods simplify real-world complexities, highlighting artificial intelligence’s role in precise cooling load forecasting for energy-efficient building management. This study evaluates Naive Bayes-based models for estimating building cooling load consumption. These models encompass a single model, one optimized with the Mountain Gazelle Optimizer and another optimized with the horse herd optimization algorithm. The training dataset consists of 70% of the data, which incorporates eight input variables related to the geometric and glazing characteristics of the buildings. Following the validation of 15% of the dataset, the performance of the remaining 15% is tested. Based on analysis through evaluation metrics, among the three candidate models, Naive Bayes optimized with the Mountain Gazelle Optimizer (NBMG) demonstrates remarkable accuracy and stability, reducing prediction errors by an average of 18% and 31% compared to the other two models (NB and NBHH) and achieving a maximum R2 value of 0.983 for cooling load prediction.

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

Studying the energy use behavior of occupants is crucial for accurately predicting building energy consumption. However, few studies have considered the impact of occupant behaviors on energy consumption in university dormitories. The objective of this study is to establish an agent-based model of energy consumption for university dormitories based on energy use behavior. The dormitories of a typical university in Sichuan, China, were subdivided into three clusters using a two-step cluster analysis. Subsequently, the energy use behaviors of occupants in each type of dormitory were characterized to establish a stochastic energy use behavior model. On the basis of the above, NETLOGO was used to construct an agent-based model for dormitories' energy consumption to dynamically simulate energy use behavior. The accuracy of the model was verified by comparing the simulated values with the measured data. Finally, a building-energy-friendly retrofit scheme was proposed, and it was found that the optimized dormitory reduced energy consumption by 16.07%. Therefore, the results can provide information support for energy-saving decisions during the early design and retrofit phases of buildings. With the popularity of centralized supply, the research methodology may provide an extensive reference for energy management policies and sustainable strategies in the building sector. [ABSTRACT FROM AUTHOR]

Research simultaneously examining building energy consumption and outdoor thermal comfort within urban environments remains limited. Few studies have delved into the sensitivity of design parameters based on building energy consumption and outdoor thermal comfort. The purpose of this study is to investigate the correlations between urban morphological design parameters and performance indicators, focusing on building energy consumption and outdoor thermal comfort (UTCI), across different urban block layouts in hot-humid regions, like Guangzhou. By establishing six fundamental morphological models—three individual unit layouts and three group layouts—the research explores both control and descriptive parameters through extensive simulation studies. Scatter plot visualizations provide insights into the impacts of various design parameters on energy consumption and UTCI, facilitating a comprehensive analysis of trends and quantitative relationships. Additionally, the study conducts sensitivity analyses on design parameters under different layout conditions to highlight their influences on target performance indicators. The findings reveal common trends, such as the significant impacts of plan dimensions and the Floor Area Ratio (FAR) on energy efficiency and outdoor comfort, as well as differential patterns, such as the varying sensitivities of the Shape Factor (S/V) and the Sky View Factor (SVF), across individual and collective layouts. Ultimately, this study offers a nuanced understanding of urban block morphology's role in creating sustainable, comfortable, and energy-efficient urban environments, providing valuable guidelines for urban form design in hot-humid climates. [ABSTRACT FROM AUTHOR]

In Iran, the intensity of energy consumption in the building sector is almost 3 times the world average, and due to the consumption of fossil fuels as the main source of energy in this sector, as well as the lack of optimal design of buildings, it has led to excessive release of toxic gases into the environment. This research develops an efficient approach for the simulation-oriented Pareto optimization (SOPO) of building energy efficiency to assist engineers in optimal building design in early design phases. To this end, EnergyPlus, as one of the most powerful and well-known whole-building simulation programs, is combined with the Multi-objective Ant Colony Optimization (MOACO) algorithm through the JAVA programming language. As a result, the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties. To evaluate the effectiveness of the developed method, it was performed on a residential building located in the hot and semi-arid region of Iran. To obtain the optimum configuration of the building under investigation, the building rotation, window-to-wall ratio, tilt angle of shading device, depth of shading device, color of the external walls, area of solar collector, tilt angle of solar collector, rotation of solar collector, cooling and heating setpoints of heating, ventilation, and air conditioning (HVAC) system are chosen as decision variables. Further, the building energy consumption (BEC), solar collector efficiency (SCE), and predicted percentage of dissatisfied (PPD) index as a measure of the occupants' thermal comfort level are chosen as the objective functions. The single-objective optimization (SO) and Pareto optimization (PO) are performed. The obtained results are compared to the initial values of the basic model. The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs, owing to the lower value of the deviation index. Moreover, the optimal solutions extracted through the PO are depicted in the form of Pareto fronts. Eventually, the Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) technique as one of the well-known multi-criteria decision-making (MCDM) methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions. Further, the results of PO show that although BEC increases from 136 GJ to 140 GJ, PPD significantly decreases from 26% to 8% and SCE significantly increases from 16% to 25%. The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making. In addition, the method presented in this study can be applied to different types of buildings in different climates. [ABSTRACT FROM AUTHOR]

Residential buildings contribute 30% of the UK's total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy-efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings' energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the EPC (Energy Performance Certificate) dataset for residential buildings and standard assessment procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building's energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R2) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. All summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings. [ABSTRACT FROM AUTHOR]

Cooling load estimation is crucial for energy conservation in cooling systems, with applications like advanced air-conditioning control and chiller optimization. Traditional methods include energy simulation and regression analysis, but artificial intelligence outperforms them. Artificial intelligence models autonomously capture complex patterns, adapt, and scale with more data. They excel at predicting cooling loads influenced by various factors, like weather, building materials, and occupancy, leading to dynamic, responsive predictions and energy optimization. Traditional methods simplify real-world complexities, highlighting artificial intelligence's role in precise cooling load forecasting for energy-efficient building management. This study evaluates Naive Bayes-based models for estimating building cooling load consumption. These models encompass a single model, one optimized with the Mountain Gazelle Optimizer and another optimized with the horse herd optimization algorithm. The training dataset consists of 70% of the data, which incorporates eight input variables related to the geometric and glazing characteristics of the buildings. Following the validation of 15% of the dataset, the performance of the remaining 15% is tested. Based on analysis through evaluation metrics, among the three candidate models, Naive Bayes optimized with the Mountain Gazelle Optimizer (NBMG) demonstrates remarkable accuracy and stability, reducing prediction errors by an average of 18% and 31% compared to the other two models (NB and NBHH) and achieving a maximum R2 value of 0.983 for cooling load prediction. [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]

Accurate and effective building energy consumption prediction is an important basis for carrying out energy-saving evaluation and the main basis for building energy-saving optimization design. However, due to the influence of environmental and human factors, energy consumption prediction is often inaccurate. Therefore, this paper presents a building energy consumption prediction model based on an attention mechanism, time convolutional neural (TCN) network fusion, and a bidirectional gated cycle unit (BIGRU). First, t-distributed stochastic neighbor embedding (T-SNE) was used to preprocess the data and extract the key features, and then a BIGRU was employed to acquire past and future data while capturing immediate connections. Then, to catch the long-term dependence, the dataset was partitioned into the TCN network, and the extended sequence was transformed into several short sequences. Consequently, the gradient explosion or vanishing problem is mitigated when the BIGRU handles lengthy sequences while reducing the spatial complexity. Second, the self-attention mechanism was introduced to enhance the model's capability to address data periodicity. The proposed model is superior to the other four models in accuracy, with an mean absolute error of 0.023, an mean-square error of 0.029, and an coefficient of determination of 0.979. Experimental results indicate that T-SNE can significantly improve the model performance, and the accuracy of predictions can be improved by the attention mechanism and the TCN network. [ABSTRACT FROM AUTHOR]

The building sector's energy consumption remains a significant problem, requiring the development of powerful energy management systems to improve energy efficiency. The main aim of this research is to forecast energy use through indoor temperature, relative humidity, time, and time lag as crucial factors. The research project aims to enhance power consumption forecasting throughout the operating phase of buildings by using advanced machine learning (ML) regression models such as XGBoost, LightGBM, Extra Trees, and Random Forest. The dataset includes 10 days during which the building's operation testing took place, illustrating the complexities of real-world conditions. The inclusion of time-related characteristics and time delay arises as a critical element in enhancing prediction accuracy, emphasizing the temporal sensitivity of power use. A hyperparameter tuning method was used to improve the performance of the implemented ML algorithms. The results emphasize the effectiveness of Extra Trees as the most suitable model for forecasting power consumption in building operations with an accuracy of 88.41%. [ABSTRACT FROM AUTHOR]

Developing an accurate energy prediction model is one of the most efficient methods to explain building energy efficiency. Such a prediction is a way to conserve energy and help make better design decisions in order to reduce energy consumption. To date, many studies have built models for predicting the building energy consumption of existing buildings, but few studies have considered energy efficiency at the early design stage with the possible variations of the parameters that might affect the overall energy consumption. In this study, supervised machine learning algorithms were used with parametric design tools to address energy usage in the early design decision stages due to its high speed, accuracy, and capacity to handle nonlinear interactions between variables. Additionally, the Grasshopper plugin for Rhino was used as a parametric design tool, and the honeybee plugin was used to anticipate energy usage for various architectural considerations of residential units in Jordan. The outcomes of the honeybee model were used to predict energy consumption using ANN and SVM as machine learning algorithms. The findings showed that the ANN model with two hidden layers had the highest accuracy for training and testing (0.972%). Therefore, this model provides an efficient, accurate way to predict the energy consumption for residential buildings. [ABSTRACT FROM AUTHOR]

The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption. The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.

The field of non-residential buildings is very heterogeneous. This results in a compartmentalized approach that prevents desired energy efficiency improvements from being achieved at a large scale. This paper proposes a transversal approach to energy efficiency based on the aggregation of building typologies and the integration of the scales of analysis.Upon energy and occupancy analogies we define shared buildings as buildings characterized by a low energy intensity, the predominance of HVAC systems on energy uses, and a large and variable occupancy. This concept gathers in itself a considerable energy efficiency potential, as shared buildings are found to account for 35% of non-residential energy consumption. We overview energy efficiency hotspots and operational criticalities in shared buildings and outline the development of a framework that is fit-for-purpose for the quantification of the energy efficiency potential at multiple scales.Improvement in ventilation energy management in a university stock is used to exemplify the approach: first, we apply dynamic simulation to a single building obtaining 65% HVAC energy savings; then, we generalize the results to the property stock obtaining reductions from 190 to 130 kWh/(m2y) in energy intensity, simultaneously fulfilling the desired energetic and economic targets.

The global drive for sustainable development and carbon neutrality has heightened the need for energy-efficient buildings. Photovoltaic buildings, which aim to reduce energy consumption and carbon emissions, play a crucial role in this effort. However, the potential of the building envelope for electricity generation is often underutilized. This study introduces an efficient hybrid method that integrates Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. This integrated approach was used to optimize the external envelope structure and photovoltaic components, leading to significant reductions: overall energy consumption decreased by 41% (from 105 kWh/m2 to 63 kWh/m2), carbon emissions by 34% (from 13,307 tCO2eq to 8817 tCO2eq), and retrofit and operating costs by 20% (from CNY 13.12 million to CNY 10.53 million) over a 25-year period. Sensitivity analysis further revealed that the window-to-wall ratio and photovoltaic windows play crucial roles in these outcomes, highlighting their potential to enhance building energy performance. These results confirm the feasibility of achieving substantial energy savings and emission reductions through this optimized design approach.

Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE's ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively).

Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the Owl Search Algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process. [ABSTRACT FROM AUTHOR]

As socioeconomic development continues, the issue of building energy consumption has attracted significant attention, and improving the thermal insulation performance of buildings has become a crucial strategic measure. Simultaneously, the application of solid waste in insulation materials has also become a hot topic. This paper reviews the sources and classifications of solid waste, focusing on research progress in its application as insulation materials in the domains of daily life, agriculture, and industry. The research shows that incorporating household solid waste materials, such as waste glass, paper, and clothing scraps into cementitious thermal insulation can significantly reduce the thermal conductivity of the materials, leading to excellent thermal insulation properties. Insulation materials prepared from agricultural solid waste, such as barley straw, corn stalk, chicken feather, and date palm fibers, possess characteristics of lightweight and strong thermal insulation. Industrial solid waste, including waste tires, iron tailings, and coal bottom ash, can also be utilized in the preparation of insulation materials. These innovative applications not only have positive environmental significance by reducing waste emissions and resource consumption, but also provide efficient and sustainable insulation solutions for the construction industry. However, to further optimize the mix design and enhance the durability of insulation materials, continuous research is required to investigate the mechanisms through which solid waste impacts the performance of insulation materials. [ABSTRACT FROM AUTHOR]

Since decoration is an essential part of buildings, the carbon emissions generated by decoration work should not be ignored. In recent years, prefabricated decoration has attracted much attention as efforts are made to pursue green, low-carbon, and waste-reducing buildings. However, research on carbon emissions assessment of prefabricated buildings has focused mainly on the structural aspect of prefabricated buildings, with few studies having considered prefabricated decoration. This study therefore focuses on assessing the carbon emissions of prefabricated decoration from the life cycle perspective of a case study residential building and explores the potential for reducing carbon emissions by decorating buildings with prefabricated components. The results show that using prefabricated decoration in the case study building reduced carbon emissions by 29.08% at the building material production stage compared to traditional decoration, and using an optimized design of prefabricated decoration, the building's energy consumption over its design life could reduce carbon emissions by 1046 kgCO2/m2. These findings demonstrate the benefits of prefabrication decoration for reducing carbon emissions. This study provides decoration companies with robust data and insights to guide future decisions and practices, helping to transform and achieve the carbon neutrality goal for the building decoration industry. [ABSTRACT FROM AUTHOR]

Built environment plays a significant role in optimizing building energy consumption. However, few studies have explored the comprehensive effect between built environment metrics on building energy consumption. Thus, this study aims to explore interrelationships between built environment on building energy consumption focused on moderating effect. In this study, we established a built environment measure system from the perspective of land use and land cover, landscape structure and building configuration. This study explored the correlation between built environment and building energy consumption and analyzed the moderating effect of building configuration emphatically. Results show that: for integrated grids group, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) have a positive influence and impervious area (IA) has a negative influence, with NDVI has the greatest impact. Building floor (BF), building coverage ratio (BCR) and aspect ratio can weaken the positive relationship between NDVI and energy use intensity of grid ( EUI grid ). BCR weakens the positive effect of MNDWI on EUI grid . The moderating effect of building configuration on EUI varies in the same grid group and among different grid groups. For sample 1, BCR inhibits the negative effect of mean perimeter–area ration (PARA-MN) on EUI grid . For sample 2, BF promotes the negative effect of number of patches and land use richness index (R) on EUI grid . And sky view factor inhibits the positive effect of IA on EUI grid . This study reveals the pathways of built environment on building energy consumption. As a result, the keys of optimizing building energy consumption are the reasonable planning and optimization of the urban built environment of different land cover. [ABSTRACT FROM AUTHOR]