This review systematically examines the fragmented research landscape on building energy system resilience, focusing on its definition, assessment, improvement, and optimization. An analysis of 145 publications reveals the various definitions related to power outage capacity, thirty assessment parameters, and twenty-eight improvement strategies. And approximately half of these publications further explore the optimization of these strategies. The analyses identify several key challenges, including a lack of metrics covering different perspectives (different stages, events, and resilience dimensions), integrated demand-supply-storage improvement strategies, optimization of existing strategies, and a focus on climate adaptability, vulnerable groups (such as the elderly), and emerging technologies (such as AI). Based on these challenges, a key issue is to integrate concepts including climate adaptability, reliability, robustness, and traditional narrow resilience. And then, five future directions are proposed: increasing focus on specific subjects and conditions, emphasizing responses to different and integrated disasters, developing comprehensive assessment metrics, developing full-cycle resilience strategies with multi-objective optimization, and integrating AI-based tools. In response to challenges and future directions, this review attempts to propose a novel resilience assessment, improvement, and optimization roadmap based on multi-type flexible energy resources. This roadmap can address various indicators, events, phases, systems, and strategies, employing light-weight assessment parameters and parallel improvement strategies. A case study confirms its feasibility, demonstrating a 21.13% reduction in annual costs under normal conditions and savings of 34,900 USD during a typhoon day. This study provides valuable insights and actionable guidance for developing the definition, assessment, improvement, and optimization of building energy system resilience.

Global warming and the intensified urban heat island effect are degrading urban thermal environments, threatening residential living conditions and public health. As vital communities, educational campuses are also exposed to extreme heat, and those in hot-arid zones suffer most acutely. Enhancing campus outdoor thermal settings safeguards well-being, expands activity areas, boosts efficiency, and cuts energy use and carbon emissions. This research investigates the determinants that shape the outdoor environment of arid-region campuses and proposes optimization strategies. Multiple outdoor spaces with distinct functional roles on the Shihezi University campus were chosen for in situ monitoring of key thermal indicators. The work quantified how shading performance, building morphology, and surrounding vegetation influence summer thermal comfort and tested improvement scenarios through calibrated simulations. Results show that vegetation is the most effective regulator: tall trees can reduce air temperature by up to 4 degrees C and raise relative humidity by roughly 7 %, markedly elevating perceived comfort. Permeable paving consistently outperforms asphalt, concrete, and limestone under open-sky and shaded conditions. Building enclosure exerts only a modest influence, with semienclosed courtyards proving marginally preferable to fully enclosed forms. Additionally, the spatiotemporal pattern of campus microclimates is strongly governed by incoming solar radiation and prevailing wind direction: peak temperatures occur in vast open areas. At the same time, the highest humidity is observed within building shadows. These findings provide a scientific foundation for designing healthy, comfortable, resilient campus thermal environments in arid regions.

Current indoor thermal environment management methods primarily rely on uniform control modes, making it difficult to accommodate individual temperature preferences in spaces occupied by multiple occupants. To resolve the conflict between individual thermal comfort variations and overall energy optimization during the cooling season, this paper proposes a collaborative control strategy integrating personal comfort model (PCM) with personalized environmental control system (PECS). The PCM was developed and validated using field experimental data through machine learning, and the results demonstrate that it more accurately reflects individual thermal demands. Specifically, control based on this model increases the proportion of thermal sensation votes within the thermal neutral zone by 29.2% compared to PMV-based control. In addition, by integrating radiant panel terminals as PECS with background conditioning systems, the system effectively accommodates variations in thermal comfort while also offering significant energy-saving potential. Through EnergyPlus simulation, it is found that the proposed cooperative control strategy not only ensures personal thermal comfort, but also reduces the building cooling load by about 24.9% to 28.4%. This energy-saving advantage becomes more pronounced under low occupancy conditions. Specifically, when occupancy reaches 25%, the system cooling load decreases by 38.4% to 40.0% compared to traditional air conditioning modes. This study provides a technically sound and practically valuable pathway for achieving refined thermal environment control for multiple occupants while enhancing building energy efficiency.

Achieving carbon neutrality requires large-scale retrofitting of existing residential buildings to zero-energy house standards. However, conventional season classifications often fail to capture the real impact of climate variability on energy use patterns, limiting the effectiveness of retrofit strategies. This study introduces a climate-responsive seasonal framework to assess the energy and economic sustainability of a fully electrified, grid-connected detached house in Saga City, Japan. Using meteorological data (2003-2024) and detailed energy use records (2022-2023), Seasonal-Trend Decomposition using Loess and appliance-level load analysis were applied to reveal seasonal load dynamics and photovoltaic performance. Findings show that local climate-based season definitions significantly improved the interpretation of seasonal load patterns, with the within-season variability of air-conditioning demand decreasing by more than 20 % compared with the conventional definition, effectively reducing misclassification during transitional months. A mismatch between photovoltaic generation and household load led to a low self-consumption ratio (12-22 %). Although feed-in revenues were stable, the system investment (4.92 million JPY) could not be recovered within the contractual period. The winter demand-photovoltaic mismatch calls for season-aligned storage planning, not just added capacity. The study highlights the need for season-sensitive analysis and integrated storge planning to guide sustainable residential retrofits.

Addressing the global energy crisis and excessive emissions has heightened the critical importance of reducing energy consumption and carbon emissions in the building sector, making accurate building energy forecasting a fundamental research focus. While existing methods predominantly prioritize forecasting accuracy by advanced algorithms, considerations of computational efficiency and model interpretability remain scarce. To bridge this gap, this study proposes a novel forecasting method that simultaneously optimizes for accuracy, efficiency, and interpretability. The method integrates three strategies: (a) incorporating weighted occupant behavior probabilities as novel inputs; (b) incorporating physics-informed loss function calculated by thermal resistancecapacitance (R-C) models; and (c) developing a hybrid CNN-LSTM-Attention algorithm that integrates convolutional neural networks and an attention mechanism with a long short-term memory network. Validation of 48 cases from four office buildings shows the proposed method significantly enhances performance. These three strategies reduce the mean absolute percentage error (MAPE) by 25.78 % and the coefficient of variation of the root mean square error (CV-RMSE) by 21.31 %, and average contributions are 40 %, 15 % and 45 % for Strategies (a)-(c), respectively. Strategy (c) is the primary contributor to efficiency gains, which can reduce time consumption by 7343.69s and 146.81s compared to Transformer-LSTM-Adaboost and LSTM-SSA, respectively. Strategies (a) and (b) improve interpretability by embedding occupant behavior patterns and thermal constraints. Moreover, the priority of these strategies for buildings with varying behavioral and functional complexities is analyzed. In summary, based on theoretical considerations and practical validation, the proposed method can improve the accuracy, efficiency, and interpretability simultaneously.

Against the backdrop of global climate change exacerbating extreme heat events in arid regions, this study investigates the pedestrian microclimatic differences between traditional and modern transportation hubs in Urumqi, a large city in China's BWk climate zone, and comprehensively examines the pedestrian outdoor thermal comfort characteristics. By integrating multidimensional data from in-situ monitoring, thermal perception surveys, and microclimate simulations, a dynamic assessment model based on Physiological Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) was developed. The results indicate that in summer, within the transportation hub buildings in Urumqi, the neutral temperature ranges of PET and UTCI are 19.9-28.3 degrees C and 25.7-30.0 degrees C, respectively, while their acceptable temperature ranges are 13.8-33.3 degrees C and 18.5-31.1 degrees C, respectively. Meanwhile, the thermal stress grades of transportation hub spaces in Urumqi during summer were revised. In addition, with a prediction accuracy of 28.9%, UTCI was identified as the optimal index applicable to Urumqi in summer. Finally, through comparisons with different hot-arid cities, it is found that the BWk climate zone exhibits a wider threshold span and a low-temperature adaptive shift compared to the BWh climate zone and hot-humid cities, revealing the regional thermal perception differences in arid regions. This research quantifies key thermal benchmark parameters and establishes a thermal safety threshold model tailored to arid transportation spaces. It provides theoretical guidance for enhancing thermal comfort of pedestrian microclimate and optimizing the design of outdoor areas in transportation hubs, thereby strengthening their resilience and adaptability to future climate change.

The construction industry is a key industry in global energy consumption. Large-scale exhibition buildings usually have high energy consumption, and their architectural design is often large-scale, complex in form, rich in space, and diverse in equipment, requiring more cooling, lighting, management, and other methods to meet indoor comfort and exhibit display requirements. This article takes the large-scale exhibition building case A in the hot summer and warm winter zone as the research object for analysis. Through field research and observation, it studies the use of equipment in the building during the summer season, proposes optimization strategies for the use of building equipment, and uses instruments to collect actual data on the indoor environment after renovation. Finally, the analysis and discussion are conducted based on the research observation results and measured data, with a view to providing reference for the design of large-scale exhibition buildings and similar functional types of buildings.

Urban residential morphology significantly impacts Land Surface Temperature (LST). This study focuses on four residential spatial layouts in Xi’an, a city frequently affected by extreme heat waves. Using ArcGIS and SPSS for correlation analysis, we explore the influence mechanisms of different building morphologies on LST at the block scale across four seasons. The results indicate that the row layout has the greatest impact on LST, with PE and AS exerting the most influence in the enclosed layout, primarily during summer. Building Orientation (SO) shows higher significance in the row layout. The Shape-Size Index (SSI) exhibits the strongest correlation in the enclosed layout during spring and summer. Building Coverage Ratio (BCR) is highest in the row layout but does not differ significantly from other layouts. Building Proximity (PROX) has a minimal effect in traditional layouts but shows a higher correlation in other configurations. Mean Building Height (MBH) demonstrates the strongest correlation in the row layout. Sky View Factor (SVF), Shape Coefficient (SC), and Building Porosity (POR) are most correlated in the row layout, with SVF being significant in autumn, POR in summer, and SC remaining relatively stable across seasons. These findings theoretically elucidate the impact mechanisms of building morphology on the thermal environment.

Thermal improvement of urban squares is often assessed by reduced thermal stress, while less attention is given to whether interventions remain compatible with the spatial and functional attributes of squares. A benchmark-informed, site-constrained framework was developed for three representative squares in Dalian, China by integrating field measurements, questionnaires, logistic regression thermal benchmarks, and ENVI-met simulations. The results showed that the summer neutral and preferred physiological equivalent temperatures (PET) of sun-exposed respondents were 23.4 °C and 22.3 °C, respectively. All three squares remained far above both reference values, with the strongest daytime thermal stress around 14:00. The weighted average PET was highest in C square, followed by B square and A square. Under the tested summer typical meteorological day (TMD) assumptions, Alb0.6 produced the lowest mean PET among the A square scenarios, while localized treatment reduced the intervention extent without providing additional square-wide cooling. All five B square scenarios were functionally admissible, but only Layout 2 and Water 1 produced directional reductions in mean PET. Mat2 produced the lowest mean PET among the tested C square scenarios. However, the overall PET reduction remained limited, and none of the tested scenarios approached benchmark thermal conditions. These findings indicate that thermal improvement in urban squares should not be understood as unrestricted PET minimization, but as a process conditioned by spatial form, functional demands, and implementation constraints. This perspective provides a more realistic basis for thermal adaptation in urban squares under local climatic and site-specific constraints.

Building energy management is gradually transferring from isolated demand-side management or supply-side optimization toward demand-supply-storage collaboration. Existing studies may have gaps in supply-demand-storage integrated optimization methods and synergy effect evaluation, and the performance of optimization solvers and load simulation processes also needs to be further improved. This study proposes a building energy demand-supply-storage integrated optimization method, that incorporats the proposed improved JSA solver, human-centric flexible load management, and PV-battery optimization. On this basis, a grid-connected campus electricity system is adopted as the case study to evaluate the synergistic effects under different user-participation willingness rates and seasonal conditions. The results show: (1) the demand-supply-storage collaboration can reduce daily total costs by 16.75%–28.59% (mean: 22.28%) and 9.973–22.295 t/day of CO2 emissions. Flexible load management contributes 6.83%–9.22% of the total cost reduction, whereas PV-battery optimization contributes 10.07%–23.14%. (2) The improved JSA algorithm integrates chaotic initialization, Levy flight, and adversarial learning, showing superior convergence performance, solution quality, and computational efficiency compared with recent solvers. (3) Smart EV charging, AC start-up temperature optimization, and plug load management can all reduce costs, with benefits increasing alongside higher participation willingness. (4) Finally, all flexible load management strategies exhibit positive synergy with PV-battery optimization, although the enhancement varies considerably across seasons and regulation strategies. Based on the synergy effects, in energy system planning, evaluation, and retrofitting, in addition to resource scale, supply-demand matching may be a critical factor influencing synergistic benefits.

Rapid urbanization has intensified air pollution, quantitative understanding of how multidimensional urban morphology drives pollutant variability across seasons and spatial regimes remains limited. Study integrates Kmeans clustering, a CatBoost-based explainable machine learning framework, and SHAP analysis to quantify nonlinear relationships between 27 urban morphology indicators and six major air pollutants across four seasons at a 1000 m resolution. Model achieved robust predictive performance R2 exceeding 0.80 in winter. Global SHAP analysis revealed a dominant contribution from Natural Environment and Meteorological Environment factors, jointly accounting for 60 to 85% of total feature importance. Elevation, precipitation, wind speed, and solar radiation are the features with the highest explanatory power. Socioeconomic and Activities factors contributed 3 to 10%, and Land-Cover and Architectural Form generally remained below 15%. Spatial clustering identified six urban morphology types with distinct pollution regimes and driver sensitivities. Global and clustering perspectives of nonlinear SHAP dependence revealed threshold, including DEM is 500 m or 1000 m, PREC is 13 mm and 15 mm, and WS thresholds is 2 or 2.75 m/s, beyond which pollutant responses shift markedly. LISA-based spatial autocorrelation analysis demonstrated strong hotspot clustering in urban cores. Peripheral regions were dominated by low-value clusters and non-significant patterns, indicating pronounced urban-rural gradients in pollution mechanisms. Overall, our study provides an interpretable framework for disentangling the nonlinear, spatially heterogeneous drivers of urban air pollution. The results highlight the dominant role of topography-meteorology interactions and reveal threshold effects, offering insights for different urban space types of pollution mitigation and climate-adaptive urban planning.

Extreme heat in arid and hot regions presents significant challenges to thermal comfort and the sustainability of tourism. This study creates an optimized framework for thermal comfort, incorporating various data sources, field measurements, and microclimate simulations. Three representative spatial types were chosen as a case study, and specific thermal comfort optimization strategies of Water, Plant, Material, and Sunshade Facilities were evaluated. Results indicate remarkable spatial heterogeneity in the thermal environment at the macro scale, with extreme heat predominantly affecting rocky and sandy surfaces, while areas characterized by green spaces and water bodies demonstrate marked cooling effects. A significant negative correlation was found between vegetation cover and surface temperature, whereas elevation and topography showed a positive correlation. At the micro scale, during peak heatwave hours from 13:00 to 16:00, the implementation of adaptive strategies led to improvements in the Ultra-Temperature Climate Index (UTCI) in over 90% of commercial (Type 05) and public spaces (Type 10). Notably, 10.6%-18.5% of Type 10 areas experienced UTCI reductions exceeding 6 degrees C, and improvements in Type 05 reached at least 15%, with the maximum UTCI reduction recorded at 14.64 degrees C. Among the strategies, the Plant consistently yielded the strongest and most widespread cooling effects, with pools providing better optimization intensity than linear waterways, while high-albedo pavements offered limited improvement. This study enhances the understanding of thermal dynamics within arid-hot tourism environments and presents a quantitative, localized adaptive framework aimed at fostering climate-responsive design and sustainable tourism planning.

As global warming escalates, high-intensity heat waves are increasingly common, raising concerns about their impact on environmental systems and public health. This article focuses on Turpan City, known for having the highest summer temperatures in China, emphasizing its largest transportation hub, Turpan North Station. The study employs field research and data measurement to analyze the local summer climate conditions. Additionally, simulation methods were utilized to assess the spatial and temporal changes in the area's thermal environment. The research also evaluates six design scenes featuring various configurations of blue-green infrastructure, using the Universal Thermal Climate Index (UTCI) for evaluation. The results show that the average radiation temperature (Tmrt) becomes the primary factor influencing the UTCI. The incorporation of tall trees in varying proportions leads to a substantial reduction in Tmrt, with the greatest decrease amounting to 6.79 degrees C. Furthermore, through the strategic allocation of urban blue-green infrastructure, the UTCI values are decreased by 1.33 degrees C relative to the actual situation and by 1.73 degrees C when compared to simulations lacking bluegreen infrastructure. Moreover, the duration of "extreme heat stress" is reduced by a duration of 2 hours. This study also suggests targeted optimization and improvement strategies based on the characteristics of various functional zones surrounding transportation stations. Overall, it provides a robust theoretical foundation and practical guidance for the scientific planning and effective arrangement of the outdoor thermal environment in rail transit buildings in dry and hot regions.

This study explores key factors influencing Aging-in-Place Attachment (AiPA) among older adults in Macau’s high-density community spaces, emphasizing interactions between the built environment, behavior, and psychology. A multidimensional framework evaluates environmental, behavioral, human-factor, and psychological contributions. A mixed-methods, multisource approach was employed. This study measured spatial characteristics of nine public spaces, conducted systematic behavioral observations, and collected questionnaire data on place attachment and aging intentions. Eye-tracking and galvanic skin response (GSR) captured visual attention and emotional arousal. Hierarchical regression analysis tested the explanatory power of each variable group, supplemented by semi-structured interviews for qualitative depth. The results showed that the physical environment had a limited direct impact but served as a critical foundation. Behavioral variables increased explanatory power (~15%), emphasizing community engagement. Human-factor data added ~4%, indicating that sensory and habitual interactions strengthen bonds. Psychological factors contributed most (~59%), confirming AiPA as a multidimensional construct shaped primarily by emotional and social connections, supported by physical and behavioral contexts. In Macau’s dense urban context, older adults’ desire to age in place is mainly driven by emotional connection and social participation, with spatial design serving as an enabler. Effective age-friendly strategies must extend beyond infrastructure upgrades to cultivate belonging and interaction. This study advances environmental gerontology and architecture theory by explaining the mechanisms of attachment in later life. Future work should explore how physical spaces foster psychological well-being and examine emerging factors such as digital and intergenerational engagement.

Global climate change intensifies the impact of high-temperature disasters, particularly in urban areas with diverse architectural designs that often fail to prioritize comfort. This research uses inversion techniques and field studies to examine outdoor climate comfort across different land uses in Dalian City, China. It aims to enhance thermal environments in cold coastal cities. Results indicate commercial buildings offer the best outdoor thermal comfort, while plaza areas fare worst. Optimization strategies for outdoor thermal environments differ depending on the specific influential factors in various land use scenarios. Additionally, the degree of optimization can affect the effectiveness of thermal comfort optimization. Regarding architectural layout, reducing the street's height-to-width ratio by 0.25 results in a 1 degrees C increase in the Universal Thermal Climate Index (UTCI) value. Furthermore, planting tall deciduous trees around educational buildings effectively reduces the UTCI value in shaded areas, surpassing the benefits of architectural layout optimization. The study uses microclimate simulations to enhance outdoor thermal comfort in cold coastal cities, offering valuable planning insights based on data and optimization.

China's aging population is a pressing issue, with the need for comfortable living environments for older adults being paramount to their health and well-being. A study was conducted in Dalian, China, involving physical environment measurements and surveys in nursing homes and residential buildings. The investigation focused on changing indoor thermal environments and older adults' subjective sensations. The study explored physical environment satisfaction and actual thermal comfort ranges in different aging modes and space characteristics. The results show that older people spend most time in bedrooms, and dissatisfaction with the thermal environment in winter and summer is high, reaching 42% and 74%, respectively. Residential buildings generally have higher indoor temperatures than nursing homes, with a mean PMV difference of 0.9 in winter. Furthermore, thermal comfort models show that the comfort zones for nursing homes are more comprehensive in winter but smaller in summer. This study provides valuable information for future research on thermal comfort of older adults in different aging modes, facilitating the creation of healthier indoor thermal environments.

Urban open space is an important place for people's outdoor activities. Previous studies have paid little attention to outdoor comfort in arid-hot areas of China. This study focuses on the comfort evaluation of outdoor activity spaces of four different attribute sites in Shihezi City, an arid-hot region, residential (R), commercial (C), educational (E) and green square (G). Outdoor thermal environment parameters were collected and subjective questionnaires were administered in summer, and 460 valid questionnaires were collected. Physiologically equivalent temperature (PET) and universal thermal climate index (UTCI) were used as evaluation indicators and outdoor thermal benchmarks were determined. In summer people's thermal preference is skewed towards heat. There are differences in thermal comfort for different attribute lands. We found that the green square is the most comfortable among the four attribute site categories evaluated, with 90 % of PET and UTCI acceptable temperatures ranging from 16.1-34.5 degrees C and 16.0-31.0 degrees C. In addition, we found that UTCI was more effective than PET in assessing outdoor thermal comfort in arid-hot areas. This study contributes to developing effective strategies and guidelines for urban renewal and block improvement.

Block thermal environments affect public health and the sustainable development of cities. However, the study of spatial heterogeneity of block morphology in thermal environments needs further research, and exploring a new classification system of block morphology will also be beneficial to urban management. Taking 1426 blocks of Dalian city as an example, this paper proposed a new classification system of block morphology (HDR system) by a two-step cluster. Pearson coefficient, ordinary least squares (OLS) model, and geographically weighted regression (GWR) model were applied to explore the mechanisms of building height (BH), building density (BD), and plot ratio (PR) on land surface temperature (LST) globally (non-classification) and locally (classification system HDR1-5) respectively. The results are as follows: 1) Low-floor buildings dominate Dalian's blocks. 2) Greater intensity of block development is associated with greater LST. 3) BH, BD, and PR show significant spatial heterogeneity on LST. 4) LST is most notably affected by BD. 5) HDR5 is recommended as the preferred block plan to face the worsening of the thermal environment. The results of this study provide direct references and implications for city managers and designers who want to develop practical and sustainable policies and norms for urban renewal and new blocks.