High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R2 to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing.

Socio-technical dynamics of energy consumption in low-income housing in Amman, Jordan

Modeling and experimentation of a solar wall using single glazing as a thin double skin to reduce the energy consumption of old brick houses

Natural ventilation plays a crucial role in enhancing thermal comfort and reducing energy consumption in low-cost housing. This study investigates the effectiveness of natural ventilation strategies in Ajegunle, Lagos State, focusing on building orientation, window configuration, and structural permeability. A quantitative research approach was adopted, utilizing a structured questionnaire administered to 150 randomly selected respondents. Data analysis, conducted using descriptive statistics and regression analysis, revealed a negative correlation (r = -0.62) between natural ventilation efficiency and reliance on mechanical cooling systems. The study found that 73% of respondents in well-ventilated homes used fans and air conditioners less frequently, whereas 81% in poorly ventilated homes depended on these appliances daily. Additionally, factors such as financial constraints (64%), landlord unwillingness (58%), and security concerns (46%) were identified as barriers to implementing effective passive cooling solutions. Despite these challenges, the study underscores the potential of natural ventilation in improving living conditions in low-cost housing. The findings offer valuable insights for architects, urban planners, and policymakers, emphasizing the need for design innovations and policy support to enhance passive cooling strategies in affordable housing developments. Future research should explore advanced passive cooling techniques and their long-term impacts on energy efficiency and occupant well-being.

ABSTRACT To reduce energy consumption in container houses in cold regions, the container house in the Yanqing competition zone of the Beijing 2022 Olympic Winter Games was selected as the research subject. The orthogonal experimental design method conducted a multi-factor combination study on container house envelope structure. Using the L25(56) orthogonal array, the factors significantly impacting building energy consumption were selected, including wall insulation, roof insulation, floor insulation, external window construction, window-to-wall ratio, and external door construction. Each factor was set at five levels. The findings indicate heating energy consumption accounts for the largest portion of the container house's annual whole energy consumption. Enhancing envelope insulation significantly lowers heating energy consumption, thereby reducing total annual energy consumption. The optimal combination scheme identified in this study includes 153.6-mm rock wool board for wall insulation, 195.0 mm rock wool board for roof insulation, 171.6-mm rock wool board for floor insulation, high-permeability double-layer Low-E glass for windows, 55% window-towall ratio, and a 75-mm rock wool sandwich door. This solution achieves the lowest annual whole energy consumption (3354.1 kWh), the lowest carbon emissions (2387.7 kgCO₂), and the highest economic benefit, reducing energy consumption by 43.2% compared to the initial construction state.

Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data at the individual level, but pay less attention to preserving the population-level properties on the entire dataset. Such population-level properties include value distributions for each dimension and distributions of certain functional dependencies (e.g., cross-correlation, CC) between different dimensions. For instance, when generating house energy consumption TS data, the value distributions of the outside temperature and the kitchen temperature should be preserved, as well as the distribution of CC between them. Preserving such TS population-level properties is critical in maintaining the statistical insights of the datasets, mitigating model bias, and augmenting downstream tasks like TS prediction. Yet, it is often overlooked by existing models. Hence, data generated by existing models often bear distribution shifts from the original data. We propose Population-aware Diffusion for Time Series (PaD-TS), a new TS generation model that better preserves the population-level properties. The key novelties of PaD-TS include 1) a new training method explicitly incorporating TS population-level property preservation, and 2) a new dual-channel encoder model architecture that better captures the TS data structure. Empirical results in major benchmark datasets show that PaD-TS can improve the average CC distribution shift score between real and synthetic data by 5.9x while maintaining a performance comparable to state-of-the-art models on individual-level authenticity.

In the process of deepening the strategy of rural revitalization in China, the transformation of rural houses is an important part of promoting rural revitalization. Rural houses are the living places of the majority of rural residents, and there have been problems of poor thermal comfort and high energy consumption in the Xuzhou area for a long time. At present, the starting point of domestic research on the transformation of rural houses is mostly single-objective optimization of thermal comfort or energy conservation, and few studies involve these two aspects at the same time. In this study, based on field investigation, questionnaire surveys, software simulation, orthogonal testing, and other research methods, an adaptive thermal comfort model for rural residents in the Xuzhou area was established, and the factors affecting the indoor thermal comfort and energy consumption of the rural houses were simulated. In order to jointly improve indoor thermal comfort and reduce energy consumption in rural houses, an optimal transformation scheme suitable for existing rural houses in the Xuzhou area was proposed. By comparing the data of indoor thermal comfort and annual energy consumption of a typical rural house before and after the transformation, and then estimating the cost required for the transformation of the house, the advantages of the transformation scheme in terms of effectiveness and economy were verified.

In this paper, field tests, questionnaire surveys, and DesignBuilder were used to analyse the indoor thermal environment and energy consumption of traditional houses in a traditional ethnic minority village of Western Sichuan Plateau of China, The results showed that during the summer test period, the outdoor temperature range was 9.3–7.8 °C and the relative humidity range was 53.5–67.4%, while the indoor temperature range of the tested room was 13.3–2.3 °C, and the relative humidity range was 69.1–83.0%. The humidity is high, and the thermal environment does not meet the requirement of local standard. Therefore, corresponding energy-saving optimization measures are proposed. In the winter heating building model data, compared with the heat load before optimization, the energy saving reaches about 56.5%. In addition, the carbon emissions and economic suitability of different heating methods were evaluated. Electric heating, coal-fired heating and biomass heating have payback periods of 11 years, 24 years and 6 years respectively. With perspective focusing on the special regional and ethnic characteristics of the plateau, this research aims to promote energy conservation and sustainable development of local traditional buildings of ethnic minorities, and help improve the living environment of the Sichuan Plateau. In the future, a long-term monitoring mechanism can be established to continuously track residential buildings after the adoption of optimization measures to evaluate the actual effect of these measures.

The key role of buildings in tackling climate change has gained global recognition. To avoid unnecessary costs and time wasted, it is important to understand the conditions and energy usage for existing housing stock to identify the most important features affecting energy consumption and to guide the relevant retrofit measures. This paper investigated how the spatial, morphological and thermal characteristics of residential houses contribute to housing energy consumption. Additionally, it presents a rapid assessment tool using minimum data input to answer two main questions: 1) What type of properties may need retrofit? 2) What building elements/features may be prioritised to be retrofitted? A case study was performed with around 143,000 residential properties in Sheffield. An automated machine approach was applied which successfully estimated the energy consumption of target buildings with an score of 0.828. Permutation feature importance and partial dependence of the features were examined against energy consumption. The results indicate that housing sizes and conditions of the external walls are found to be the most important features when estimating the energy consumption of residential buildings in Sheffield. Relatively larger and older detached houses in neighbourhoods with higher build density may benefit the most from home upgrading projects for energy consumption reduction.

The building envelope, through its components, affects the thermal performance and energy consumption, mainly through windows, which provide easy access for heat gain or loss. In this context, Windows play an essential role in building energy efficiency, influencing heating and cooling requirements. Three key window parameters must be optimized at the design stage to take advantage of solar gain while limiting overheating: orientation, glazing type, and window-to-wall ratio. Therefore, this article aims to assess the impact of different window parameters on the energy used for heating and cooling of an individual housing in Guelma, located in the north-east of Algeria. Four orientations, three glazing types, and ten window-wall ratio scenarios were studied based on a parametric simulation using Rhinoceros 3D and its Grasshopper plug-in. The research results showed that the window to the north has the highest overall energy use while the window to the south has the lowest. Also, the rise in the window/wall ratio and the increase in energy usage are therefore positively correlated. Moreover, double glazing low-emissivity was found to be the most effective in terms of energy savings across all orientations. These savings were especially notable for high window/wall ratios, with the southern orientation reaching up to 35.21%, the western orientation, 25.13%, the eastern orientation 27.05%, and the northern orientation 23.24%. . This study provides guidelines for window design to reduce energy use, maximize thermal comfort, and enhance the energy efficiency of buildings.

Purpose Housing energy consumption is a significant contributor to climate change. Encouraging the adoption of energy-efficient products can be an effective way to reduce energy consumption. The impacts of social influences, such as peer effects and social norms, on energy efficiency adoptions were identified; however, these social influences are not quantified and compared with each other or with other influences. This study aims to investigate the choice of energy-efficient product adoption with different costs and how different social influences affect the choice through different processes and paths. Design/methodology/approach Two stated choice experiments were employed in Wuhan, China, to examine the impact of social influences on energy-efficient product adoption in low-cost and high-cost scenarios. Appliance packages (including fridges and washing machines) and heating and cooling systems were used for the two cost scenarios, respectively. The social influences are evaluated in three aspects: positive versus negative information, physical versus online social networks and peer effects versus social norms. Findings The study revealed how various factors, including social influences, impact energy-efficient product choices. Research results show that: (1) social influences have greater and wider impacts in the low-cost scenario than in the high-cost scenario; (2) negative information decreases the adoption of low-cost energy-efficient products, while positive information boosts high-cost energy-efficient product adoption and (3) people value the information provided by those they know personally and are more influenced by physical social networks. Originality/value This study contributes to a better understanding of social influence in energy-efficient product adoption with different costs. This study provides a comprehensive framework to investigate social influences comparing the impact of different processes, paths and types of information. The findings can also provide practical implications for policymakers to accelerate the energy transition in the built environment.

A simplified method for calculating air conditioning load of multi-family housing community considering the spatiotemporal distribution of occupants

Limited material options and economic conditions significantly restrict the potential for energy efficiency improvements in rural houses in China’s cold regions. It is worth exploring how to propose suitable energy-saving renovation plans for rural houses in cold regions under practical constraints. By using Grasshopper within Rhinoceros 8 software, an algorithm integrates material selection, energy consumption calculations, and economic analysis. The method efficiently generates thermal optimization schemes, providing insights into energy use, costs, and payback periods. In a case study of a typical rural house in Daqing City, the optimized scheme achieved over 70% energy savings compared to traditional homes, with renovation costs amounting to less than 40% of residents’ annual income and a 2-year payback period. This significant improvement highlights the potential of the proposed method in enhancing the energy efficiency and economic viability of rural house renovations.

ABSTRACT As the residential construction sector grows more quickly than other building sectors in Oman and other GCC nations, the proportion of total energy consumption in housing rises. The native inhabitants have implemented various strategies to enhance the thermal conditions in structures. The windcatcher was one of those methods used in hot climates when it was vital to achieve thermal comfort. Computational Fluid Dynamics (CFD) is used in this research to find the best orientation of a windcatcher attached to a modern house in Muscat. In addition, thermal comfort and indoor air quality based on CO2 concentration were also studied. This paper tries to fill the gap in research on using windcatchers with modern houses and find the best design for such windcatchers. Results indicate that placing a windcatcher with a rotation of 30° from the north towards the east will give better air velocity than a windcatcher with angles of 0°, 45°, and 60°. Furthermore, it was found that the windcatcher improved the Predicted Mean Vote (PMV) by 34% and the Predicted Percentage of Dissatisfaction (PPD) by 48%. In addition, a modern house with a windcatcher reduced CO2 concentration by more than 5% compared to a house without a windcatcher.

In this present study Energy Performance Index (EPI) of the traditional rural and modern urban houses located in and around Mandi – Sundernagar town at Himachal Pradesh, India, having composite climate, are assessed based on the household energy consumption data for the year 2021 and 2022. The EPI plays pivotal role as an indicator to assess the energy efficiency of different kind of buildings by setting up a practical holistic benchmark for building designers and other professionals to reduce and optimize the operating energy footprint of the building. Further, the effect and correlation of various influencing factors on the annual household energy consumption and EPI are also analyzed through regression analysis to develop models for the prediction of future trend of household energy consumption pattern. The calculated average EPI value of modern houses is found as 39.24 KWH/m²/year (range: 29.43 – 50.53 KWH/m²/year). In contrast, average EPI value of traditional houses is calculated as 7.89 KWH/m²/year (range: 6.34 – 10.36 KWH/m²/year). The study shows that the mean total annual energy consumption of modern houses is 5.4 times higher than that of the traditional houses; the mean EPI of modern houses is 5 times higher than that of the traditional houses; and the mean EPI/person of modern houses is 5 times higher than that of the traditional houses in the study area. Linear regression analysis has shown that total annual household energy consumption and EPI can be well predicted by the factors – floor area, annual average household income, and total number of different appliances.

Optimization of energy consumption in residential housing within the framework of energy sustainability strategies. A case study in the Canary Islands

Este estudio analiza el comportamiento térmico de una vivienda construida con quincha en una zona bioclimática IVa, en la precordillera andina donde el clima es templado frío seco. Se utilizaron sensores higrotérmicos para medir la temperatura y la humedad en el interior y el exterior de la vivienda durante 10 días representativos de las estaciones frías, cálidas e intermedias. Los resultados muestran que la vivienda tiene un buen desempeño térmico, logrando amortiguar las grandes amplitudes diarias de temperatura hasta 17°C en invierno y 6,8°C en verano (con T°ext. min.= -4.1°C en invierno y T° ext. máx.= 40.1°C en verano). También se reduce la oscilación térmica interior, manteniendo la temperatura estable y con un retardo de 2:10 horas en el día más frío. El efecto másico del muro de tierra de quincha presenta mayores ventajas en invierno. Sin embargo, no se alcanzan los niveles de confort interior en verano (solo en un 23%) y en invierno (solo en un 12%) a través del acondicionamiento térmico pasivo. En la estación intermedia (otoño) se logran los niveles de confort en un 82%. Estos resultados pueden contribuir a la reducción del consumo energético en viviendas construidas con recursos naturales como la tierra en zonas similares.

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model’s exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms.

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

In regions of China experiencing severe cold, the duration of the winter heating season significantly contributes to elevated heating energy consumption in rural dwellings. This study focuses on typical brick-and-concrete rural homes in the Wusu area. Utilizing the Rhino–Grasshopper parametric modeling platform, it aims to minimize heating-related carbon emissions and the overall costs associated with retrofitting. The approach involves improving the insulation properties of the building envelope to reduce energy requirements. Additionally, the study incorporates solar photovoltaic systems atop rural homes, building upon low-carbon, passive, energy-efficient design principles. By examining the influence of various factors on rural housing energy consumption, the research employs the entropy weight method to identify the most effective design solutions. The goal is to explore strategies for the energy-efficient retrofitting of rural dwellings in areas faced with harsh winter conditions, aligning with the objectives and preferences of Applied Sciences. The simulation results reveal the following: (1). In comparison with the baseline scenario, 42.2% of the optimized solutions within the Pareto frontier satisfy the current standards for 75% energy savings in energy-efficient residential design. (2). The lowest recorded thermal consumption index for the buildings can reach 12.427 W/m2, at which point the rate of energy savings is elevated to 79.5%. (3). Within the solutions identified by the Pareto frontier, 80% exhibit initial investments that are lower than the cost savings over the lifecycle due to reduced energy consumption (dCg < 0), demonstrating the economic feasibility of the proposed retrofitting strategies.