Pub Date : 2026-02-05DOI: 10.1016/j.enbuild.2026.117113
Manuel Kipp, Ruya Wang, Klaus Bengler
This paper presents an AI-based model for optimizing heating, ventilation, and air conditioning (HVAC) settings to improve thermal comfort in electric vehicles under winter conditions and to estimate the associated power consumption. Unlike conventional HVAC systems that primarily rely on convective heating, the investigated concept combines convective airflow with nine radiant heating panels to enhance comfort and energy efficiency. Equivalent temperature (ET) was employed as an objective thermal comfort metric, and an XGBoost (Extreme Gradient Boosting) model was trained to predict ET for 16 body regions, achieving a high accuracy (coefficient of determination ). A Random Forest model was applied to relate fan speed and damper settings to mass flow. Validation experiments confirmed that the optimized HVAC settings maintained thermal comfort, with at least 50% of local body regions and 100% of upper and lower body averages within the neutral comfort zone. The approach demonstrated potential power savings of up to 240 W compared to convection-dominant strategies. These findings highlight the potential of combining AI with hybrid HVAC concepts to improve passenger comfort and reduce energy consumption in future automated electric vehicles.
{"title":"Optimizing thermal comfort in highly automated vehicles: An AI-Based HVAC management approach with radiant panels for winter conditions","authors":"Manuel Kipp, Ruya Wang, Klaus Bengler","doi":"10.1016/j.enbuild.2026.117113","DOIUrl":"10.1016/j.enbuild.2026.117113","url":null,"abstract":"<div><div>This paper presents an AI-based model for optimizing heating, ventilation, and air conditioning (HVAC) settings to improve thermal comfort in electric vehicles under winter conditions and to estimate the associated power consumption. Unlike conventional HVAC systems that primarily rely on convective heating, the investigated concept combines convective airflow with nine radiant heating panels to enhance comfort and energy efficiency. Equivalent temperature (ET) was employed as an objective thermal comfort metric, and an XGBoost (Extreme Gradient Boosting) model was trained to predict ET for 16 body regions, achieving a high accuracy (coefficient of determination <span><math><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.96</mn></mrow></math></span>). A Random Forest model was applied to relate fan speed and damper settings to mass flow. Validation experiments confirmed that the optimized HVAC settings maintained thermal comfort, with at least 50% of local body regions and 100% of upper and lower body averages within the neutral comfort zone. The approach demonstrated potential power savings of up to 240 W compared to convection-dominant strategies. These findings highlight the potential of combining AI with hybrid HVAC concepts to improve passenger comfort and reduce energy consumption in future automated electric vehicles.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"357 ","pages":"Article 117113"},"PeriodicalIF":7.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172678","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}
Pub Date : 2026-02-05DOI: 10.1016/j.enbuild.2026.117099
Qicong Wu , Yang Ni , Zhou Fang , Shenghua Liu , Yi Jiang
The building sector faces the dual challenge of minimizing energy consumption while maintaining indoor environmental quality in the face of escalating global climate change and urbanization. Active-passive coupling offers a promising solution by combining the advantages of passive and active design. However, most studies evaluate passive strategies and active systems in isolation, failing to quantify trade-offs between free-running and air-conditioned periods and the passive impact on the active system. To bridge this gap, this paper presents a systematic review and statistical analysis of 152 publications from 2010 to 2024. It examines the interaction mechanisms of five design categories, including building configuration, transition space, spatial organization, enclosure structure, and working conditions. Key findings reveal that static parameters often exhibit conflicts between different periods, whereas dynamic strategies or parameters effectively resolve these contradictions. The study concludes that active–passive coupling design requires the zoning and real-time switching of building operation modes across both spatial and temporal dimensions. Existing studies still lack integration of building energy simulation with transient computational fluid dynamics, the synergistic effects between vertical and horizontal spatial organization, and control logic integrated with occupant behavior. This study establishes an active–passive coupling framework, constructs a dynamic-static parameters integration method, formulates decision-making guidance, and identifies future research directions, offering actionable insights for architects and engineers to achieve robust, low-energy, and comfortable building design.
{"title":"Active-passive coupling in building design: a review of parameter interactions for energy performance and thermal and visual comfort","authors":"Qicong Wu , Yang Ni , Zhou Fang , Shenghua Liu , Yi Jiang","doi":"10.1016/j.enbuild.2026.117099","DOIUrl":"10.1016/j.enbuild.2026.117099","url":null,"abstract":"<div><div>The building sector faces the dual challenge of minimizing energy consumption while maintaining indoor environmental quality in the face of escalating global climate change and urbanization. Active-passive coupling offers a promising solution by combining the advantages of passive and active design. However, most studies evaluate passive strategies and active systems in isolation, failing to quantify trade-offs between free-running and air-conditioned periods and the passive impact on the active system. To bridge this gap, this paper presents a systematic review and statistical analysis of 152 publications from 2010 to 2024. It examines the interaction mechanisms of five design categories, including building configuration, transition space, spatial organization, enclosure structure, and working conditions. Key findings reveal that static parameters often exhibit conflicts between different periods, whereas dynamic strategies or parameters effectively resolve these contradictions. The study concludes that active–passive coupling design requires the zoning and real-time switching of building operation modes across both spatial and temporal dimensions. Existing studies still lack integration of building energy simulation with transient computational fluid dynamics, the synergistic effects between vertical and horizontal spatial organization, and control logic integrated with occupant behavior. This study establishes an active–passive coupling framework, constructs a dynamic-static parameters integration method, formulates decision-making guidance, and identifies future research directions, offering actionable insights for architects and engineers to achieve robust, low-energy, and comfortable building design.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117099"},"PeriodicalIF":7.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185860","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}
Pub Date : 2026-02-05DOI: 10.1016/j.enbuild.2026.117104
Kumar Biswajit Debnath , Natalia Pynirtzi , Jane Scott , Colin Davie , Ben Bridgens
Climate change and severe urban heat stress in South Asian megacities are driving an amplified reliance on energy-intensive air conditioning, necessitating urgent low-carbon cooling solutions. This study addresses this challenge by reinterpreting the traditional jaali, a perforated passive-cooling screen, using mycelium-based composites (MBCs) to create a novel, climate-responsive, low-carbon façade system: bio-jaali. We assessed the performance of the bio-jaali through a holistic approach, combining historical climate data analysis (New Delhi, 1991–2019), dynamic building energy simulations, and laboratory bio-fabrication and hygrothermal testing. This integrated methodology is a key achievement, bridging materials science with dynamic simulation to improve building-scale performance. The climate analysis revealed a 60% increase in ‘danger-level’ heat-stress hours over the 28 years. Dynamic simulation results showed that replacing the conventional sandstone jaali with the bio-jaali yielded substantial thermal benefits: a 3.5°C (10%) reduction in the annual average indoor operative temperature and a drop in peak summer indoor temperatures by up to 14.8°C. Consequently, the annual cooling energy demand was lowered by 50.4%. Furthermore, laboratory cyclic humidity tests demonstrated the MBCs’ potential for evaporative cooling, confirming they remained dimensionally stable (<3% change) while absorbing up to 17.2% moisture. The bio-jaali is highlighted as a culturally rooted, bio-based solution that significantly reduces reliance on active cooling. This research contributes new knowledge on the building-scale performance, climate adaptability, and cyclic hygrothermal stability of MBC facades. We position the bio-jaali as a robust prototype for integrating passive and adaptive thermal regulation, advancing circular construction practices for sustainable architecture in heat-stressed urban environments.
{"title":"Bio-jaali: Reimagining vernacular passive cooling screens with mycelium-based composites","authors":"Kumar Biswajit Debnath , Natalia Pynirtzi , Jane Scott , Colin Davie , Ben Bridgens","doi":"10.1016/j.enbuild.2026.117104","DOIUrl":"10.1016/j.enbuild.2026.117104","url":null,"abstract":"<div><div>Climate change and severe urban heat stress in South Asian megacities are driving an amplified reliance on energy-intensive air conditioning, necessitating urgent low-carbon cooling solutions. This study addresses this challenge by reinterpreting the traditional jaali, a perforated passive-cooling screen, using mycelium-based composites (MBCs) to create a novel, climate-responsive, low-carbon façade system: bio-jaali. We assessed the performance of the bio-jaali through a holistic approach, combining historical climate data analysis (New Delhi, 1991–2019), dynamic building energy simulations, and laboratory bio-fabrication and hygrothermal testing. This integrated methodology is a key achievement, bridging materials science with dynamic simulation to improve building-scale performance. The climate analysis revealed a 60% increase in ‘danger-level’ heat-stress hours over the 28 years. Dynamic simulation results showed that replacing the conventional sandstone jaali with the bio-jaali yielded substantial thermal benefits: a 3.5°C (10%) reduction in the annual average indoor operative temperature and a drop in peak summer indoor temperatures by up to 14.8°C. Consequently, the annual cooling energy demand was lowered by 50.4%. Furthermore, laboratory cyclic humidity tests demonstrated the MBCs’ potential for evaporative cooling, confirming they remained dimensionally stable (<3% change) while absorbing up to 17.2% moisture. The bio-jaali is highlighted as a culturally rooted, bio-based solution that significantly reduces reliance on active cooling. This research contributes new knowledge on the building-scale performance, climate adaptability, and cyclic hygrothermal stability of MBC facades. We position the bio-jaali as a robust prototype for integrating passive and adaptive thermal regulation, advancing circular construction practices for sustainable architecture in heat-stressed urban environments.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"357 ","pages":"Article 117104"},"PeriodicalIF":7.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147115","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}
Pub Date : 2026-02-05DOI: 10.1016/j.enbuild.2026.117112
Lukas Anselm Wille , Björn Schiricke , Kai Gehrke , Tobias Dehne , Bernhard Hoffschmidt
In this study, we build upon previous simulation research that advocates the use of infrared (IR) heaters in conjunction with IR reflective interior walls to meet heating demand in buildings. This combination allows the walls to reflect the heat emitted by the IR heaters back to the occupants in a room, rather than absorbing the radiation. As a result, the radiant temperature increases and the air temperature can be lowered in order to maintain constant thermal comfort and to reduce heat loss through the building envelope. We conducted experiments in a climate chamber to isolate the effects of four factors on thermal comfort: the heating power of IR heaters, the IR emittance of the interior walls, the interior wall surface temperature, and the air temperature. The emittance was modified by applying an increasing number of adhesive aluminium foil stripes. Heat conduction through the wall to the outside is not part of this study. To minimize the number of required experiments, we employed a Central Composite Design, from which we derived a response surface function. The experimental results confirm a correlation between wall emittance and occupant thermal comfort in a room, particularly at higher IR heater power levels. The Predicted Mean Vote (PMV) value increases at lower wall emittance (corresponding to higher radiant temperatures), highlighting the potential for energy savings through reduced air temperatures. However, the observed impact of low emittance surfaces on the PMV is less pronounced than previously estimated in simulation studies.
{"title":"Reduction of heating energy demand by combining IR heaters and IR reflective walls: An experimental study","authors":"Lukas Anselm Wille , Björn Schiricke , Kai Gehrke , Tobias Dehne , Bernhard Hoffschmidt","doi":"10.1016/j.enbuild.2026.117112","DOIUrl":"10.1016/j.enbuild.2026.117112","url":null,"abstract":"<div><div>In this study, we build upon previous simulation research that advocates the use of infrared (IR) heaters in conjunction with IR reflective interior walls to meet heating demand in buildings. This combination allows the walls to reflect the heat emitted by the IR heaters back to the occupants in a room, rather than absorbing the radiation. As a result, the radiant temperature increases and the air temperature can be lowered in order to maintain constant thermal comfort and to reduce heat loss through the building envelope. We conducted experiments in a climate chamber to isolate the effects of four factors on thermal comfort: the heating power of IR heaters, the IR emittance of the interior walls, the interior wall surface temperature, and the air temperature. The emittance was modified by applying an increasing number of adhesive aluminium foil stripes. Heat conduction through the wall to the outside is not part of this study. To minimize the number of required experiments, we employed a Central Composite Design, from which we derived a response surface function. The experimental results confirm a correlation between wall emittance and occupant thermal comfort in a room, particularly at higher IR heater power levels. The Predicted Mean Vote (PMV) value increases at lower wall emittance (corresponding to higher radiant temperatures), highlighting the potential for energy savings through reduced air temperatures. However, the observed impact of low emittance surfaces on the PMV is less pronounced than previously estimated in simulation studies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"357 ","pages":"Article 117112"},"PeriodicalIF":7.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172596","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}
Pub Date : 2026-02-05DOI: 10.1016/j.enbuild.2026.117110
Menghui Xiao , Cuifeng Du , Weidong Song , Yuan Wang , Zimo Shi , Yao Lu
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 (T = Tenv + aLcb), 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.
{"title":"Spatiotemporal evolution of the temperature field in cemented tailings backfill considering volume effects and its impact on the thermal environment","authors":"Menghui Xiao , Cuifeng Du , Weidong Song , Yuan Wang , Zimo Shi , 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 (CTB<sub>40</sub>) exhibited a peak temperature 6.9 ℃ higher than the smallest sample (CTB<sub>10</sub>). Peak temperature at different locations exhibits a power-law relationship with characteristic length (<em>T = T<sub>env</sub> + aL<sub>c</sub><sup>b</sup></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":"2026-02-05","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}
Recent architectural research emphasizes energy efficiency and daylight optimization as principles long embedded in vernacular design. This study proposes a data-driven image-to-simulation framework that integrates deep learning, building performance simulation, and sensitivity analysis to evaluate the environmental performance of traditional Iranian window typologies, including Orosi, Shabak, and Simple Traditional. Two pretrained convolutional neural networks (CNNs), ResNet50 and EfficientNet-B0, were employed to extract simulation-relevant geometric and optical parameters directly from façade images using explicit operational definitions, eliminating the need for manual three-dimensional modeling while enabling physically meaningful representation of complex vernacular geometries. The extracted parameters were incorporated into EnergyPlus and Radiance simulations to assess energy demand, daylight availability, and glare risk without reliance on CAD-based reconstruction. The simulation results indicate that Shabak windows achieved the most balanced performance, reducing annual cooling demand by up to 14% while maintaining spatial daylight autonomy of 72% and low glare probability, with a daylight glare probability (DGP) value of 0.25. Sensitivity analyses based on SHapley Additive exPlanations (SHAP) and Sobol methods identified Window-to-Wall Ratio (WWR) and solar heat gain coefficient (SHGC) as the dominant drivers of thermal and visual performance, while frame depth and perforation density contributed to improved daylight uniformity. To integrate these outcomes, the Window Environmental Performance Index (WEPI) was developed, through which Shabak windows achieved the highest composite score (0.78), enabling a unified, scalable, and interpretable evaluation of complex vernacular window geometries for climate-responsive design.
{"title":"From ornament to algorithm: reinterpreting vernacular windows through image-based environmental simulation","authors":"Niloufar Mosalmanfarkoosh , Mazyar Abaee , Niusha Mosalmanfarkoosh","doi":"10.1016/j.enbuild.2026.117102","DOIUrl":"10.1016/j.enbuild.2026.117102","url":null,"abstract":"<div><div>Recent architectural research emphasizes energy efficiency and daylight optimization as principles long embedded in vernacular design. This study proposes a data-driven image-to-simulation framework that integrates deep learning, building performance simulation, and sensitivity analysis to evaluate the environmental performance of traditional Iranian window typologies, including Orosi, Shabak, and Simple Traditional. Two pretrained convolutional neural networks (CNNs), ResNet50 and EfficientNet-B0, were employed to extract simulation-relevant geometric and optical parameters directly from façade images using explicit operational definitions, eliminating the need for manual three-dimensional modeling while enabling physically meaningful representation of complex vernacular geometries. The extracted parameters were incorporated into EnergyPlus and Radiance simulations to assess energy demand, daylight availability, and glare risk without reliance on CAD-based reconstruction. The simulation results indicate that Shabak windows achieved the most balanced performance, reducing annual cooling demand by up to 14% while maintaining spatial daylight autonomy of 72% and low glare probability, with a daylight glare probability (DGP) value of 0.25. Sensitivity analyses based on SHapley Additive exPlanations (SHAP) and Sobol methods identified Window-to-Wall Ratio (WWR) and solar heat gain coefficient (SHGC) as the dominant drivers of thermal and visual performance, while frame depth and perforation density contributed to improved daylight uniformity. To integrate these outcomes, the Window Environmental Performance Index (WEPI) was developed, through which Shabak windows achieved the highest composite score (0.78), enabling a unified, scalable, and interpretable evaluation of complex vernacular window geometries for climate-responsive design.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117102"},"PeriodicalIF":7.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185942","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}
Developing the integrated energy system (IES) has emerged as a critical pathway toward achieving zero-carbon communities. However, existing studies still face challenges such as limited carbon reduction effectiveness and weak inter-regional coordination, hindering further progress. Herein, this study introduces a community integrated energy system (CIES) that incorporates carbon capture systems (CCS) and inter-community energy exchange and sharing mechanisms. A multi-energy coupling model encompassing residential, commercial, and industrial zones is established. A bi-level optimization framework for simultaneous capacity configuration and operational scheduling is formed, which strengthens the synergy between planning and operation and enables efficient cross-zone coordination. Furthermore, a time-varying CCS scheduling strategy accounting for real-time electricity pricing and dynamic carbon emissions is proposed, effectively enhancing both the system operational flexibility and the carbon reduction efficiency. Case study results demonstrate that the proposed system enhances renewable energy utilization, carbon reduction, and energy self-sufficiency by 14.48%, 38.19%, and 26.15%, respectively, while regional energy coordination increases by 16.45%. These findings validate the technical feasibility of the proposed CIES model and provide a practical solution for advancing zero-carbon community development.
{"title":"A bi-level optimization framework for integrated energy system with time-varying carbon capture and multi-energy sharing networks: Towards zero-carbon community","authors":"Yang-wen Wu, Xiao-li Chen, Si-qi Gong, Xiong Zhang, Xin-yue Zhou, Qiang Lu","doi":"10.1016/j.enbuild.2026.117106","DOIUrl":"10.1016/j.enbuild.2026.117106","url":null,"abstract":"<div><div>Developing the integrated energy system (IES) has emerged as a critical pathway toward achieving zero-carbon communities. However, existing studies still face challenges such as limited carbon reduction effectiveness and weak inter-regional coordination, hindering further progress. Herein, this study introduces a community integrated energy system (CIES) that incorporates carbon capture systems (CCS) and inter-community energy exchange and sharing mechanisms. A multi-energy coupling model encompassing residential, commercial, and industrial zones is established. A bi-level optimization framework for simultaneous capacity configuration and operational scheduling is formed, which strengthens the synergy between planning and operation and enables efficient cross-zone coordination. Furthermore, a time-varying CCS scheduling strategy accounting for real-time electricity pricing and dynamic carbon emissions is proposed, effectively enhancing both the system operational flexibility and the carbon reduction efficiency. Case study results demonstrate that the proposed system enhances renewable energy utilization, carbon reduction, and energy self-sufficiency by 14.48%, 38.19%, and 26.15%, respectively, while regional energy coordination increases by 16.45%. These findings validate the technical feasibility of the proposed CIES model and provide a practical solution for advancing zero-carbon community development.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117106"},"PeriodicalIF":7.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186273","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}
The increasing penetration of photovoltaic systems in public buildings highlights the need for demand-side management strategies that can improve self-consumption, reduce grid dependence, and enhance economic performance under realistic operating conditions. In Mediterranean climates, where photovoltaic generation and building demand often exhibit temporal mismatch, empirical evidence on the effectiveness of demand side management in public-building photovoltaic-battery microgrids remains limited, particularly across different countries and tariff structures. This study evaluates a unified demand side management framework based on intra-day load shifting that combines peak shaving and valley filling with seasonally adapted time-of-use tariffs. The methodology is applied to four real public-building microgrids in Cyprus, Greece, Israel, and Italy using validated 15-minute operational datasets. Three flexibility levels (5%, 10%, and 15% of daily demand) are examined to quantify demand side management impacts on netload profiles, self-consumption rate, self-sufficiency rate, and annual operating costs. Results show that demand side management consistently reshapes netload profiles and reduces operating costs by 3–13% across all pilot sites, with the magnitude of technical benefits depending on photovoltaic-to-load ratios and system size. Smaller systems exhibit improvements, while larger systems mainly benefit from reduced export peaks and enhanced operational stability. The findings highlight tariff differentiation as a key driver of demand side management effectiveness with a scalable, cost-effective strategy for improving the technoeconomic performance of microgrids in Mediterranean public buildings.
{"title":"Demand-side management in photovoltaic-battery microgrids: A multi-country assessment for Mediterranean public buildings","authors":"Alexandros Arsalis , Angelos Nousdilis , Gianni Celli , Aggelos Bouhouras , Georgios Christoforidis , Susanna Mocci , George E. Georghiou","doi":"10.1016/j.enbuild.2026.117111","DOIUrl":"10.1016/j.enbuild.2026.117111","url":null,"abstract":"<div><div>The increasing penetration of photovoltaic systems in public buildings highlights the need for demand-side management strategies that can improve self-consumption, reduce grid dependence, and enhance economic performance under realistic operating conditions. In Mediterranean climates, where photovoltaic generation and building demand often exhibit temporal mismatch, empirical evidence on the effectiveness of demand side management in public-building photovoltaic-battery microgrids remains limited, particularly across different countries and tariff structures. This study evaluates a unified demand side management framework based on intra-day load shifting that combines peak shaving and valley filling with seasonally adapted time-of-use tariffs. The methodology is applied to four real public-building microgrids in Cyprus, Greece, Israel, and Italy using validated 15-minute operational datasets. Three flexibility levels (5%, 10%, and 15% of daily demand) are examined to quantify demand side management impacts on netload profiles, self-consumption rate, self-sufficiency rate, and annual operating costs. Results show that demand side management consistently reshapes netload profiles and reduces operating costs by 3–13% across all pilot sites, with the magnitude of technical benefits depending on photovoltaic-to-load ratios and system size. Smaller systems exhibit improvements, while larger systems mainly benefit from reduced export peaks and enhanced operational stability. The findings highlight tariff differentiation as a key driver of demand side management effectiveness with a scalable, cost-effective strategy for improving the technoeconomic performance of microgrids in Mediterranean public buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117111"},"PeriodicalIF":7.1,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186272","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}
Pub Date : 2026-02-04DOI: 10.1016/j.enbuild.2026.117103
Xinyi Lin , Zhe Tian , Adrian Chong , Yakai Lu , Jide Niu , Na Deng
Grey-box modeling has been widely used in building thermal modeling due to its adaptability and interpretability. The identification of model parameters mainly depends on the measured dataset, and its optimal construction is critical for ensuring model accuracy. Existing studies commonly discuss the influence of training data quantity on the model accuracy. However, the training data informativeness is always ignored, which reflects the quality and richness of information within the data samples and informs the estimates of model parameter values. Notably, the informativeness level may vary among samples, and the quantity of data does not necessarily correlate with its informativeness. Here, we propose a data informativeness evaluation method that can well select informative training data for grey-box models under different scenarios. The method establishes two evaluation criteria based on the characteristics of grey-box model: one describes the consistency between training and forecasting data distributions, and the other outlines the distribution variations within the training data. The effectiveness of the proposed method is demonstrated using data from experiment case. The results indicate that the proposed data informativeness index reflects the quality of the dataset well and has a high correlation with prediction accuracy (The Pearson correlation coefficient varies from −0.6 to −0.8). This evaluation method will be of great significance for optimizing the dataset construction of grey-box model of building thermal dynamics.
{"title":"A data informativeness evaluation method for grey-box modeling of building thermal dynamics","authors":"Xinyi Lin , Zhe Tian , Adrian Chong , Yakai Lu , Jide Niu , Na Deng","doi":"10.1016/j.enbuild.2026.117103","DOIUrl":"10.1016/j.enbuild.2026.117103","url":null,"abstract":"<div><div>Grey-box modeling has been widely used in building thermal modeling due to its adaptability and interpretability. The identification of model parameters mainly depends on the measured dataset, and its optimal construction is critical for ensuring model accuracy. Existing studies commonly discuss the influence of training data quantity on the model accuracy. However, the training data informativeness is always ignored, which reflects the quality and richness of information within the data samples and informs the estimates of model parameter values. Notably, the informativeness level may vary among samples, and the quantity of data does not necessarily correlate with its informativeness. Here, we propose a data informativeness evaluation method that can well select informative training data for grey-box models under different scenarios. The method establishes two evaluation criteria based on the characteristics of grey-box model: one describes the consistency between training and forecasting data distributions, and the other outlines the distribution variations within the training data. The effectiveness of the proposed method is demonstrated using data from experiment case. The results indicate that the proposed data informativeness index reflects the quality of the dataset well and has a high correlation with prediction accuracy (The Pearson correlation coefficient varies from −0.6 to −0.8). This evaluation method will be of great significance for optimizing the dataset construction of grey-box model of building thermal dynamics.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"357 ","pages":"Article 117103"},"PeriodicalIF":7.1,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147113","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}
Pub Date : 2026-02-04DOI: 10.1016/j.enbuild.2026.117116
Shanshan Li, Xinyue Xu, Haoran Wang, Yuheng Cao, Hongda An, Ziyang Wang, Sina A, Changhao Wang
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.
{"title":"A Non-Invasive stacked ensemble framework with shadow correction for Cost-Effective daylight illuminance prediction in buildings","authors":"Shanshan Li, Xinyue Xu, Haoran Wang, Yuheng Cao, Hongda An, Ziyang Wang, Sina A, 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":"2026-02-04","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}
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