Reliable city-scale assessment of building-surface solar potential is essential for BIPV planning in high-density cities, yet existing methods often underrepresent facades, interrupt shading continuity, and rely on static screening criteria. This study develops a physics-data hybrid framework to evaluate solar energy utilization on full building surfaces in Shenzhen. The framework integrates multisource GIS data, dynamic building-cluster partitioning with preserved cross-cluster shading, Perez-based multidimensional sky models, high-resolution surface discretization, dynamic solar energy utilization thresholds, multi-scenario energy-benefit assessment, and an interpretable XGB surrogate model. Results show that rooftops receive 203.16 TWh of actual annual solar energy, while facades receive 176.38 TWh, confirming that facade resources are too significant to neglect in dense cities. The effective utilization ratio is strongly negatively correlated with the shading ratio (Pearson’s r = −0.97). In photovoltaic electricity generation, rooftops contribute 61.55% and facades contribute 38.45%. Under relaxed dynamic thresholds, the effective utilization ratio of north facades increases from 0.19 to 0.88, indicating that refined thresholds reveal substantial latent deployable potential and improve seasonal supply-demand evaluation. Under the theoretical full-surface deployment scenario and the assumed installation and efficiency settings, BIPV generation could cover 108.68% of Shenzhen’s building operational electricity demand. The XGB model achieves strong predictive performance (R² = 0.957–0.993). SHAP analysis identifies radiation type, dynamic threshold, and temporal setting as the primary drivers of model outputs, while morphological variables show secondary but context-dependent effects. Overall, the proposed framework provides a scalable tool for differentiated BIPV deployment and low-carbon urban energy planning in high-density cities.

Achieving carbon mitigation while enhancing indoor environment quality presents a dual challenge for building sector. The occupant-centric part-time-local-space (PTLS) environmental control strategy offers a promising approach by conditioning only occupied subzones during use. Its effective implementation requires fine-grained understanding of spatiotemporal occupancy patterns beyond the room scale. However, existing individual-scale studies often overlook co-occupancy behavior in multi-occupant scenarios, leading to unclear environmental control demands considering all occupants. To address this gap, this study develops a spatiotemporal occupancy analysis approach using high-resolution positioning data from multiple occupants. This approach quantifies localized occupancy characteristics and overlaps between occupants to identify typical environmental demand scenarios (specifying timing, spatial scope, and involved occupants), thereby guiding the design of flexible, demand-responsive conditioning systems. On this basis, an empirical analysis of four representative households was conducted. Results reveal distinct spatiotemporal co-occupancy patterns across functional zones. Specifically, for the measured cases, the dining subzone demonstrated peak concurrence during mealtimes with average occupancy duration increased by 26.3% despite low spatial overlap. In contrast, the sofa subzone exhibited pronounced nighttime occupancy with significant spatial overlap, expanding required conditioning space by 1.4–2.9 times and prolonging occupancy duration by 50%. These findings demonstrate the fine-grained spatiotemporal analysis gives insights to the design of localized environmental control systems aligned with actual household occupancy patterns, thereby enhancing the energy-saving potential of PTLS operation.

Occupant-centric localized heating/cooling is crucial for advancing building carbon neutrality and enhancing habitation quality. This strategy hinges on achieving precise match between thermal supply and individual demand across both temporal and spatial scales, thereby minimizing unnecessary energy consumption. However, current research mainly relies on room-scale analyses that overlook fine-grained behavioral variabilities and personalized spatial preferences, constraining the development of refined environmental control systems. To address this gap, this study presents an occupant-centric method for indoor occupancy pattern analysis, introducing a Present Demand–Next Demand segment-based modeling framework that incorporates migration pathways and behavioral rhythms. It enhances the accuracy of behavioral pattern reconstruction and enables responsive, high-resolution environmental control. The framework supports the extraction of individual-scale occupancy patterns, facilitating dynamic and adaptive heating/cooling strategies. On this basis, the individual occupancy patterns of a three-person household was analyzed with field-tested positioning data. Results show that Resident Zones (RZs) account for over 85% of dwelling time while occupying only a small spatial fraction, indicating energy-saving potential through localized regulation. Behavioral analysis further reveals that different occupants exhibit distinct spatial preferences with strong connectivity between preferred zones, and that fixed transfer tendencies occur at specific times, suggesting opportunities for personalized control strategies. Moreover, different spatial clustering methods demonstrated distinct strengths under varying activity intensities, highlighting their complementarity for individual-scale behavioral analysis. Overall, this research provides support for advancing personalized environmental control, offering actionable insights for demand-responsive systems and performance-based building simulations.

In hot climates, the large amount of cooling load in electric vehicle (EV) results in a lot of battery energy consumption, leading the decrease of driving range. With the widespread application of windows in EV, the electrochromic glass (EC) shows great prospect in lowering the cooling load. However, researches on the application of EC in EV lack the consideration of both passive cooling measures and passenger comfort, which limits the further application of EC. In this paper, we proposed an idea combining the novel techniques of both electrochromism and radiative cooling. Computational fluid dynamics (CFD) is modeled to simulate the application of electrochromic and radiative cooling coupled smart windows in hot parking conditions, exploring the improvement effect of the window on the thermal environment, comfort and energy saving of the EV. The results indicate that, under the intense sunlight with an outdoor temperature of 33 ℃, activating the air conditioning to maintain an average interior temperature of 26 ℃, the coupled windows reduced the cooling capacity of the air conditioning by 762 W compared to regular windows, which can further increase the range of EV. Meanwhile, compared to simple electrochromic fully colored glass, the integration of radiative cooling technology can lower the window surface temperature by up to 10.7 ℃. Moreover, compared to regular windows, the coupled windows lowered the standard effective temperature (SET*) for passengers by approximately 7 ℃, significantly improving comfort. These research findings are expected to provide guidance for optimizing window design and enhancing the performance of EV.