Energy and Buildings最新文献

{"title":"Comprehensive evaluation of low-GWP refrigerant alternatives for variable refrigerant flow air conditioning systems considering lubricant miscibility","authors":"Hongxia He,&nbsp;Zhao Yang,&nbsp;Yong Zhang,&nbsp;Shuping Zhang,&nbsp;Zixuan Bian,&nbsp;Cheng Liu","doi":"10.1016/j.enbuild.2026.117045","DOIUrl":"10.1016/j.enbuild.2026.117045","url":null,"abstract":"<div><div>The global phase-down of high-GWP refrigerant R410A has created an urgent need to identify environmentally sustainable alternatives for variable refrigerant flow (VRF) systems. This study develops a comprehensive multi-dimensional evaluation framework that integrates thermodynamic performance, environmental impact, safety constraints, and practical feasibility to assess five low-GWP candidates—R32, R452B, R454B, R454C, and R466A. The methodology combines steady-state cycle simulations for determining seasonal efficiency indices, life-cycle climate performance (LCCP) analysis for carbon footprint quantification, and standardized charge-safety evaluations in accordance with IEC 60335–2-40 and ISO 5149–1:2014/Amd 2:2021. In parallel, experimental investigations were performed to determine lubricant miscibility with POE68 and PVE68 oils. Results highlight distinct trade-offs among key indicators: R32 achieves the highest efficiency and lowest LCCP, yet its A2L flammability and limited lubricant compatibility necessitate enhanced safety engineering and material optimization. R454B demonstrates the best overall balance of environmental performance, miscibility, and retrofit compatibility, positioning it as the most practical near drop-in replacement. R466A provides non-flammable characteristics but suffers from poor miscibility, constraining its applicability. A semi-quantitative multi-criteria decision analysis identifies optimal replacement strategies under varying design priorities. The primary contribution of this work lies in integrating lubricant miscibility data and standardized charge-safety evaluation into a unified decision-support framework, delivering a transparent, scientifically grounded tool for selecting sustainable refrigerants and guiding the transition toward low-GWP VRF technologies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117045"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A personalized federated meta-learning approach for distributed load forecasting of buildings","authors":"Qi Chang ,&nbsp;Jie Kang ,&nbsp;Lingfeng Tang","doi":"10.1016/j.enbuild.2026.117055","DOIUrl":"10.1016/j.enbuild.2026.117055","url":null,"abstract":"<div><div>Building load forecasting is crucial to its operation optimization. Federated learning enables distributed building load forecasting while preserving data privacy. However, a global model via directly aggregating local models cannot effectively capture personalized energy consumption patterns. Moreover, fine-tuning the global model may face the risk of overwriting globally shared knowledge. To address this issue, this paper proposes a personalized federated <em>meta</em>-learning approach for distributed building load forecasting. The framework consists of two parts. The first is a federated <em>meta</em>-learning-based global model that incorporates <em>meta</em>-learned auxiliary variables to extract global model parameters optimized across buildings, thereby acquiring globally shared knowledge. The second is a multi-channel residual compensation model, trained locally to capture residuals between the global prediction and actual loads, which acquires personalized knowledge not represented by the global model. The final prediction is obtained by summing the outputs of the global and personalized models, effectively balancing global generalization and local personalization. The proposed method is validated on the real-world dataset Building Data Genome Project 2, with conformal prediction employed to quantify the model uncertainty. Experimental results demonstrate that the proposed model not only improves prediction accuracy but also provides reliable uncertainty estimation through conformal prediction-based intervals.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117055"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing glass materials for renewable-energy production through building integrated photovoltaics (BIPV) −A computational approach","authors":"Aqsa Aleem ,&nbsp;Uzma Habib ,&nbsp;Waqas Salman ,&nbsp;Muhammad Tariq Saeed","doi":"10.1016/j.enbuild.2026.117108","DOIUrl":"10.1016/j.enbuild.2026.117108","url":null,"abstract":"<div><div>Building Integrated Photovoltaics (BIPV) can transform buildings from passive energy consumers into active energy producers; however, BIPV glazing performance depends strongly on the thermal, optical, and mechanical properties of the glass materials. In practical BIPV applications, transparency and thermal stability act as prerequisite constraints, while photovoltaic (PV) efficiency is the primary optimization objective within the feasible design space. Existing studies typically evaluate these attributes independently and provide limited strategies for rapid composition optimization across large chemical spaces. To address this limitation, this work presents an integrated computational framework combining molecular dynamics (MD) simulations with a deep learning (DL) model to design and optimize multicomponent silicate glasses for BIPV applications. Using α-cristobalite SiO₂ as the base composition, the effects of Na₂O, CaO, and Al₂O₃ additives (5–50 %) on glass transition temperature (Tg), thermal conductivity, and PV cell efficiency were systematically investigated. MD simulations reveal that Na₂O acts as a strong network modifier, significantly reducing Tg at low concentrations, whereas CaO enhances mechanical stability and Al₂O₃ functions as a network intermediate. Increasing additive content leads to higher heat flux and thermal conductivity, enabling tunable thermal management to support improved PV performance. The predicted Tg values and thermal conductivity trends are consistent with reported literature, confirming the reliability of the simulation approach. The DL model, trained on melt–quench MD data, achieved high predictive accuracy for glass density, enabling rapid property prediction across composition–temperature–energy space. Error analysis and response surface mapping demonstrate strong model robustness with only localized deviations. Overall, the proposed MD–DL framework offers a rapid and cost-effective strategy for screening and tailoring glass compositions to balance structural and thermal stability with PV efficiency, accelerating the development of high-performance BIPV glazing for nearly zero-energy buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117108"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid machine learning–physics-based modeling and model predictive control of variable refrigerant flow systems in buildings","authors":"Po-Ching Hsu,&nbsp;Yunho Hwang","doi":"10.1016/j.enbuild.2026.117086","DOIUrl":"10.1016/j.enbuild.2026.117086","url":null,"abstract":"<div><div>Model predictive control (MPC) of variable refrigerant flow (VRF) in buildings requires accurate modeling of both overall power consumption and the capacity distribution among individual indoor units (IDUs). Conventional physics-based models, such as VRF-SysCurve, often struggle to capture the system dynamics or represent detailed control inputs, while purely data-driven models, though accurate, typically lack physical consistency. This study proposes a hybrid modeling framework that integrates a modified VRF-SysCurve model with a machine learning (ML) model to enhance control flexibility, predictive accuracy, and physical consistency. Control-oriented inputs selected through domain expertise enable the ML model to predict IDU capacities, which are then used by the modified VRF-SysCurve model to estimate total system power. The models are trained and validated using real-world field test data from both cooling and heating seasons, with hyperparameters optimized through Bayesian optimization. A new evaluation framework combining conventional accuracy metrics and Spearman correlation is introduced to jointly assess predictive accuracy and physical consistency. Results show that the ANN-Sub model achieves high accuracy in predicting per-IDU cooling capacity (R² ≥ 0.95). The modified VRF-SysCurve with Lasso Regression provides the best balance of accuracy, physical consistency, and simplicity for power prediction. The developed hybrid model is integrated into an MPC framework to demonstrate demand-side flexibility through multi-objective optimization of energy consumption and thermal comfort in both cooling and heating modes.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117086"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal evolution of the temperature field in cemented tailings backfill considering volume effects and its impact on the thermal environment","authors":"Menghui Xiao ,&nbsp;Cuifeng Du ,&nbsp;Weidong Song ,&nbsp;Yuan Wang ,&nbsp;Zimo Shi ,&nbsp;Yao Lu","doi":"10.1016/j.enbuild.2026.117110","DOIUrl":"10.1016/j.enbuild.2026.117110","url":null,"abstract":"<div><div>Hydration‐induced heating in cemented tailings backfill (CTB) can deteriorate the thermal environment of underground working areas. To support thermal management, the influence of placement volume on the spatiotemporal evolution of the CTB temperature field and its coupled multiphysics processes was investigated. An integrated micro–macro experimental program was conducted to analyze the spatiotemporal evolution of hydration parameters for CTB with different volumes, and to develop a temperature-field model incorporating volume effects. A coupled thermos-chemical-hydraulic-electrical (T-C-H-E) mechanism was then proposed. In-situ monitoring was undertaken to validate the model and assess environmental impact. The results indicate that volume effects significantly reshape the temperature field; both peak temperature and time-to-peak increase with volume. Specifically, the largest sample (CTB₄₀) exhibited a peak temperature 6.9 ℃ higher than the smallest sample (CTB₁₀). Peak temperature at different locations exhibits a power-law relationship with characteristic length (<em>T = T_env + aL_c^b</em>), and the spatial profile is approximately Gaussian. Microstructural tests indicate greater formation of hydration products with increasing volume, with enrichment in the center and lower region. The hydration product content in the central region is 1.73 times that of the surrounding areas. Temperature is strongly and positively correlated with hydration product yield, confirming spatial non-uniformity driven by volume effects. The proposed T-C-H-E mechanism captures this spatiotemporal coupling. In-situ validation reports that the error of the temperature field calculation model was less than 10 %, and the backfill increases the ambient temperature by approximately 4–5 ℃. These findings provide practical guidance for controlling backfill heat release and improving the mine thermal environment.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117110"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Non-Invasive stacked ensemble framework with shadow correction for Cost-Effective daylight illuminance prediction in buildings","authors":"Shanshan Li,&nbsp;Xinyue Xu,&nbsp;Haoran Wang,&nbsp;Yuheng Cao,&nbsp;Hongda An,&nbsp;Ziyang Wang,&nbsp;Sina A,&nbsp;Changhao Wang","doi":"10.1016/j.enbuild.2026.117116","DOIUrl":"10.1016/j.enbuild.2026.117116","url":null,"abstract":"<div><div>Rapid and accurate prediction of indoor daylight illuminance is pivotal for dynamically optimizing artificial lighting operation under insufficient daylight conditions, a key strategy for reducing building energy consumption. To address the limitations of existing methodologies, including the computational latency of physical simulations, the insufficient adaptability to dynamic shading, and the data complexity challenges in machine learning algorithms, this study introduces a novel non-intrusive methodological framework for rapid daylight prediction, achieved through the synergistic integration of stacked ensemble learning and shadow correction strategies. A large-scale dataset of 46,656 scenarios, encompassing diverse environmental, spatial, and building-related variables, was generated using DIALux evo software. At its core, a Bayesian-optimized stacked ensemble model, combining XGBoost and Random Forest, was developed, achieving a Mean Absolute Percentage Error (MAPE) of 1.89% and a Mean Absolute Error (MAE) of 21.95 lx under complex lighting conditions, markedly surpassing single-algorithm models. To circumvent the geometric explosion in data volume associated with incorporating occlusion parameters, a partitioned shading coverage method guided by the Bayesian Information Criterion (BIC) was further proposed. This efficient post-hoc correction strategy enhances the framework’s theoretical capability to characterize dynamic shading effects from different scenarios, thereby significantly expanding its methodological applicability. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to identify XGBoost as the primary contributor of the model and verify the model’s consistency with established daylighting principles. While validated on a simulation-based dataset, the proposed framework presents a low-cost, high-precision solution for rapid indoor daylight prediction and exhibits theoretical adaptability to complex shading scenarios, thereby offering a valuable data basis for subsequent lighting control strategies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117116"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale performance of embedded current collector cement-based double-layer capacitor for building-energy storage integration: microstructure, mechanical and electrochemical properties","authors":"Jun Tian ,&nbsp;Wenchao Zhu ,&nbsp;Xiaowei Wu ,&nbsp;Yu Zheng ,&nbsp;Weiwei Zhang ,&nbsp;Jinyun Yuan ,&nbsp;Wen-Wei Wang ,&nbsp;Hao Fu ,&nbsp;Mingyuan Liu","doi":"10.1016/j.enbuild.2026.117125","DOIUrl":"10.1016/j.enbuild.2026.117125","url":null,"abstract":"<div><div>Embedded current collector cement-based double-layer capacitors (EDLCs) can simultaneously achieve the dual functions of load-bearing and energy storage, providing a potential feasible technical path for integration of building structures and energy storage. Therefore, this study investigated the feasibility of integrating building structure and energy storage functions using EDLCs. It concentrated on exploring the effects of fiber content and fiber types (i.e., carbon fiber and steel fiber), and current collector types (i.e., steel wire mesh and carbon fiber mesh) on the microstructure, working performance, mechanical properties, and electrochemical properties of EDLCs. The results showed that carbon fibers exhibited superior electrical conductivity, mechanical performance, and electrochemical properties compared with steel fibers. Specifically, compared with the electrode with steel fibers (GP-3), the electrode with carbon fibers (TP-3) has a 94.88% reduction in resistivity and an 8.80% increase in compressive strength; correspondingly, the area capacitance of capacitor T-TP-3 was 645.81% higher than that of T-GP-3. The “point-line” conductive network constructed by carbon black and carbon fibers markedly boosted the conductivity of cement-based electrodes and the energy storage performance of EDLCs. Although the increase in content of steel fibers enhanced area capacitance of EDLCs, steel fibers were prone to corrosion in cement-based environments containing chloride ions, which ultimately limited their overall electrochemical performance. The type of current collector significantly affected the capacitance performance under different current densities, and carbon fiber mesh current collector was superior to steel wire mesh current collector. The electrochemical results indicate that the EDLCs exhibit typical electric double-layer capacitive behavior, as reflected by cyclic voltammetry curves, galvanostatic charge–discharge characteristics and impedance response. This study can provide a credible foundation and technical aid for the design and practical application of embedded current collector cement-based double-layer capacitors in integrating building structure with energy storage functions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117125"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic deployment of electric heat pumps and pit thermal energy storage for renewable energy integration and heating decarbonization","authors":"Luyao Li ,&nbsp;Jichao Zhao ,&nbsp;Zhiyong Tian ,&nbsp;Xinyu Chen ,&nbsp;Dilshod Jalilov ,&nbsp;Tukhtamurod Juraev ,&nbsp;Akbar Halimov ,&nbsp;Michael T.F. Owen","doi":"10.1016/j.enbuild.2026.117079","DOIUrl":"10.1016/j.enbuild.2026.117079","url":null,"abstract":"<div><div>With the rapid expansion of renewable energy (RE) sources such as photovoltaic (PV) and wind power (WP), managing surplus energy and mitigating renewable energy curtailment have become critical challenges. This study investigates the synergistic integration of electric heat pumps (EHPs) and pit thermal energy storage (PTES) as a power-to-heat pathway to enhance renewable energy utilization and district heating (DH) performance.. A theoretical framework and a PTES heat transfer model are developed to evaluate the impacts of EHP–PTES integration on curtailment mitigation, auxiliary heating demand, and system investment costs. Integrating EHPs with PTES increases storage temperatures, reduces reliance on backup heating, and decreases solar collector investment by up to 56%. In high-penetration RE systems, a 94,985 m³ PTES can lower curtailment rates by 32.43%, saving approximately CNY 433.61 million in curtailed energy costs, CNY 30.27 million in collector investment, and reducing auxiliary heating electricity consumption by 94.5%. Compared with electrochemical storage, power-to-gas technologies, and conventional solar district heating systems, the EHP–PTES pathway enables large-capacity, low-cost, and long-duration utilization of curtailed electricity while directly interfacing with existing district heating networks. These findings highlight the significant potential of EHP-PTES integration for optimizing RE utilization, promoting heating decarbonization, and reducing overall system costs.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117079"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for improving thermal comfort through envelope design in residential buildings: Recent developments and future directions","authors":"Arda Bayraktar,&nbsp;Berk Ekici","doi":"10.1016/j.enbuild.2026.117007","DOIUrl":"10.1016/j.enbuild.2026.117007","url":null,"abstract":"<div><div>Envelopes are vital components for improving thermal comfort in almost all building typologies. Yet, the design and analysis of envelopes are complex, as they involve multiple aspects and various parameters, ensuring comfort standards. Improving thermal comfort in residential buildings is within the scope of researchers to suggest sustainable design alternatives that consider multiple performance aspects and design parameters. Previous review articles have focused on improving thermal performance in residential buildings from the perspective of envelope technology, materials, and design strategies. However, none of them investigated current developments using artificial intelligence (AI), which inevitably supports decision-making in complex circumstances for a sustainable built environment. This review examines the contribution of AI methods, which consist of metaheuristic optimization and machine learning algorithms as sub-branches, to envelope parameters. The paper systematically reviews 95 relevant works on AI, including early approaches, to provide a comprehensive overview of current developments, following PRISMA guidelines. The results showed that early applications considered conventional approaches to improve thermal comfort and energy performance, which mostly limit the results to specified cases. On the other hand, studies utilizing AI methods dealt with numerous parameters, allowing them to cope with complex envelope systems in a reasonable amount of time. The study addresses relevant research questions related to the trends, research methods, system types, AI methods, data types, and their relation to performance and envelope parameters. The study also provides actionable insight, underlining gaps and future works for utilizing machine learning methods in the reviewed research domain.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117007"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration of key design questions in rule-based shading control for building energy load reduction","authors":"Jihyeon Cho ,&nbsp;Suyeon Bang ,&nbsp;Hoseong Lee ,&nbsp;Yeonsook Heo","doi":"10.1016/j.enbuild.2026.117038","DOIUrl":"10.1016/j.enbuild.2026.117038","url":null,"abstract":"<div><div>Automated shading can curb building energy use; however, its performance depends on the control strategy design. This study quantified how state variable, dynamicity level, and adaptation horizon shape end-use savings and visual comfort. and formulated static and dynamic rule-based strategies for exterior slat-type blinds that track the solar horizontal profile angle (SHPA). Both schemes were tuned monthly or annually. The candidate state variables include direct solar irradiance, outdoor temperature, and indoor temperature. We simulated the total energy loads (cooling, heating, and lighting) and operational behavior (mode duration and switching frequency) for the perimeter zones across the orientations. Monthly optimized static control achieved 39–41% total-load savings vs. a no-control baseline and outperformed a fixed 90° reference with dimming (∼29%). Dynamic SHPA tracking offers marginal energy gains (&lt;≈1 percentage point) but incurs orders-of-magnitude higher switching frequency. Hourly analysis showed that neither the slat modulation level nor state variable choice produced load savings because the shaded areas controlled by the strategies were not significantly different. Monthly tuning outperformed annual optimized cases by ∼ 12 percentage points, reflecting seasonal non-stationarity in sun geometry and weather. This indicates that the adaptation time interval is critical in rule-based shading control. Finally, visual comfort of static controls remained acceptable; hours with visual discomfort (daylight glare index &gt; 22) were &lt; 3% in every zone. Overall, these findings provide a practical guideline: use optimized static rules as the default approach and select the adaptation timescale according to local climate variability, while aligning shading system design with the proposed control framework.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117038"},"PeriodicalIF":7.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}