Pub Date : 2026-01-21DOI: 10.1016/j.enbuild.2026.117044
Huiyu Yan, Jili Zhang, Liangdong Ma
Building energy consumption data quality plays a critical role in analytical accuracy, yet temporal accuracy remains underexplored compared to numerical accuracy in existing research. Our analysis of monitoring platform data reveals temporal deviations inducing up to ± 20% numerical deviations of hourly data in extreme circumstances. To address this, we develop the CCKF-SPI-EP methodology, a novel multi-sensor data fusion framework that achieves simultaneous time synchronization and constraint optimization through three key techniques: a Constrained Centralized Kalman Filter framework with normalization of cumulative energy sequences, a Shape-Preserving Interpolation for monotonic time registration, and an Estimation Projection technique for constraint incorporation. Experimental results demonstrate the method’s superiority with more than 42%–67% reduction in RMSE and 59%–76% reduction in MAE on the building’s main meter compared to the best conventional method. Furthermore, we provide practical recommendations for improving data acquisition protocols to incorporate temporal accuracy into building energy data quality assessment. This work not only presents an effective correction framework but also makes forward-looking contributions in problem awareness and data quality system development for building energy informatics.
{"title":"The time deviation of building energy consumption data and its synchronization based on the CCKF-SPI-EP framework","authors":"Huiyu Yan, Jili Zhang, Liangdong Ma","doi":"10.1016/j.enbuild.2026.117044","DOIUrl":"10.1016/j.enbuild.2026.117044","url":null,"abstract":"<div><div>Building energy consumption data quality plays a critical role in analytical accuracy, yet temporal accuracy remains underexplored compared to numerical accuracy in existing research. Our analysis of monitoring platform data reveals temporal deviations inducing up to ± 20% numerical deviations of hourly data in extreme circumstances. To address this, we develop the CCKF-SPI-EP methodology, a novel multi-sensor data fusion framework that achieves simultaneous time synchronization and constraint optimization through three key techniques: a Constrained Centralized Kalman Filter framework with normalization of cumulative energy sequences, a Shape-Preserving Interpolation for monotonic time registration, and an Estimation Projection technique for constraint incorporation. Experimental results demonstrate the method’s superiority with more than 42%–67% reduction in RMSE and 59%–76% reduction in MAE on the building’s main meter compared to the best conventional method. Furthermore, we provide practical recommendations for improving data acquisition protocols to incorporate temporal accuracy into building energy data quality assessment. This work not only presents an effective correction framework but also makes forward-looking contributions in problem awareness and data quality system development for building energy informatics.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117044"},"PeriodicalIF":7.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033358","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 : 2026-01-21DOI: 10.1016/j.enbuild.2026.117042
Chuanming Li , Xiangshen Gao , Rongshan Han , Nianping Li , Jibo Long , Minghao Ren , Fajin Xu , Qingqing Long
To enhance the heating performance and building load matching capability of finned evaporator heat pumps in winter, this study proposes a frost-suppression method using an air–water source finned evaporator integrated with a hot-water coil. A computational model for this combined heat transfer unit was established. Based on the air dew point temperature, an Artificial Neural Network prediction model with a coefficient of determination R2 of 0.9998 was developed, using inlet air temperature, humidity ratio, air velocity, and hot-water temperature as input variables and refrigerant heat gain as the output. This model was employed to simulate the maximum heat supply capacity and conduct load matching analysis under frost-free evaporator operation. Results indicate that a lower air humidity ratio corresponds to greater frost-free heating potential. For instance, at 5℃ air temperature, the maximum heat supplies for humidity ratios of 0.5 g/kg and 3.5 g/kg are 2.88 W and 0.38 W, respectively. Increasing the evaporator hot-water temperature significantly boosts the heat supply under frost-free operation: at −10℃ air temperature and 0.5 g/kg humidity ratio, the maximum heating capacities with 20℃ hot water and without hot water are 12.86 W and 5.54 W, respectively. Under typical winter conditions, raising the hot-water temperature effectively enhances exerts a more substantial influence on the matching rate between heat supply and building demand than varying the air velocity: in Xiangtan, increasing the temperature from 10℃ to 20℃ improves the matching rate of 11.87% (with 20℃ hot water meeting demand for 12.85% of the heating period), while in Xi’an, the corresponding improvement is 31.66% (with 20℃ hot water satisfying 50.87% of the demand). This research provides an effective methodology for frost suppression and load matching regulation in air-source heat pumps.
{"title":"Research on frost-resistant characteristics of air-water source finned evaporator based on air dew point temperature","authors":"Chuanming Li , Xiangshen Gao , Rongshan Han , Nianping Li , Jibo Long , Minghao Ren , Fajin Xu , Qingqing Long","doi":"10.1016/j.enbuild.2026.117042","DOIUrl":"10.1016/j.enbuild.2026.117042","url":null,"abstract":"<div><div>To enhance the heating performance and building load matching capability of finned evaporator heat pumps in winter, this study proposes a frost-suppression method using an air–water source finned evaporator integrated with a hot-water coil. A computational model for this combined heat transfer unit was established. Based on the air dew point temperature, an Artificial Neural Network prediction model with a coefficient of determination R2 of 0.9998 was developed, using inlet air temperature, humidity ratio, air velocity, and hot-water temperature as input variables and refrigerant heat gain as the output. This model was employed to simulate the maximum heat supply capacity and conduct load matching analysis under frost-free evaporator operation. Results indicate that a lower air humidity ratio corresponds to greater frost-free heating potential. For instance, at 5℃ air temperature, the maximum heat supplies for humidity ratios of 0.5 g/kg and 3.5 g/kg are 2.88 W and 0.38 W, respectively. Increasing the evaporator hot-water temperature significantly boosts the heat supply under frost-free operation: at −10℃ air temperature and 0.5 g/kg humidity ratio, the maximum heating capacities with 20℃ hot water and without hot water are 12.86 W and 5.54 W, respectively. Under typical winter conditions, raising the hot-water temperature effectively enhances exerts a more substantial influence on the matching rate between heat supply and building demand than varying the air velocity: in Xiangtan, increasing the temperature from 10℃ to 20℃ improves the matching rate of 11.87% (with 20℃ hot water meeting demand for 12.85% of the heating period), while in Xi’an, the corresponding improvement is 31.66% (with 20℃ hot water satisfying 50.87% of the demand). This research provides an effective methodology for frost suppression and load matching regulation in air-source heat pumps.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117042"},"PeriodicalIF":7.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014908","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 : 2026-01-21DOI: 10.1016/j.enbuild.2026.117054
Mingyang Wang , Man-Kwan Law , Jinglei Yang , Changying Xiang
Accurate day-ahead power forecasting of rooftop photovoltaic (PV) systems is critical for grid operation, energy trading, and smart building management. While self-cleaning nanocoatings can enhance PV energy yield by mitigating dust deposition and maintaining optical transmittance, their impact on forecasting performance remains largely unexplored. This study investigates the day-ahead forecasting behavior of nanocoated and conventional rooftop PV systems using five deep learning architectures: DNN, LSTM, 1D CNN, CNN-BiLSTM, and CNN-BiGRU. A SHAP-driven Recursive Feature Elimination with Monte Carlo Cross-Validation (SHAP-RFE-MCCV) framework was developed to identify the most relevant features from hundreds of lagged meteorological and power variables. Results indicate that the nanocoated PV system achieves a net cumulative power gain of 4.65% over 51 days relative to the uncoated system, corresponding to an average daily increase of 4.71%. This period covers the entire dataset used for forecasting, providing a representative assessment of coating benefits under varied irradiance conditions. While the coating enhances energy yield, sharper power variations lead to marginally higher prediction errors, reflecting the slightly increased forecasting difficulty. Among the models, DNN consistently attains the highest accuracy (R2: 0.9289–0.9496; MAE: 0.7051–0.8148), with LSTM also showing competitive predictive capability. The SHAP-RFE-MCCV framework effectively reduces input dimensionality by over 90% while preserving strong predictive accuracy across models (R2 > 0.92). The study demonstrates that nanocoating not only improves energy generation but also alters temporal power patterns and forecastability. The proposed feature selection method offers an efficient, interpretable solution for high-dimensional PV forecasting and insights for integrating rooftop PV systems into smart grid applications.
{"title":"Day-ahead power forecasting of self-cleaning nanocoated and conventional rooftop PV systems using SHAP-RFE-MCCV feature selection and deep learning","authors":"Mingyang Wang , Man-Kwan Law , Jinglei Yang , Changying Xiang","doi":"10.1016/j.enbuild.2026.117054","DOIUrl":"10.1016/j.enbuild.2026.117054","url":null,"abstract":"<div><div>Accurate day-ahead power forecasting of rooftop photovoltaic (PV) systems is critical for grid operation, energy trading, and smart building management. While self-cleaning nanocoatings can enhance PV energy yield by mitigating dust deposition and maintaining optical transmittance, their impact on forecasting performance remains largely unexplored. This study investigates the day-ahead forecasting behavior of nanocoated and conventional rooftop PV systems using five deep learning architectures: DNN, LSTM, 1D CNN, CNN-BiLSTM, and CNN-BiGRU. A SHAP-driven Recursive Feature Elimination with Monte Carlo Cross-Validation (SHAP-RFE-MCCV) framework was developed to identify the most relevant features from hundreds of lagged meteorological and power variables. Results indicate that the nanocoated PV system achieves a net cumulative power gain of 4.65% over 51 days relative to the uncoated system, corresponding to an average daily increase of 4.71%. This period covers the entire dataset used for forecasting, providing a representative assessment of coating benefits under varied irradiance conditions. While the coating enhances energy yield, sharper power variations lead to marginally higher prediction errors, reflecting the slightly increased forecasting difficulty. Among the models, DNN consistently attains the highest accuracy (R<sup>2</sup>: 0.9289–0.9496; MAE: 0.7051–0.8148), with LSTM also showing competitive predictive capability. The SHAP-RFE-MCCV framework effectively reduces input dimensionality by over 90% while preserving strong predictive accuracy across models (R<sup>2</sup> > 0.92). The study demonstrates that nanocoating not only improves energy generation but also alters temporal power patterns and forecastability. The proposed feature selection method offers an efficient, interpretable solution for high-dimensional PV forecasting and insights for integrating rooftop PV systems into smart grid applications.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117054"},"PeriodicalIF":7.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014824","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 : 2026-01-21DOI: 10.1016/j.enbuild.2026.117041
Rohit Thakur , Anil Kumar
Rapid urbanization and the increasing impact of climate change have amplified the need for climate-resilient strategies in Indian cities. Building codes and green rating systems play a pivotal role in shaping sustainable urban development. This article systematically analyzes major frameworks, including the Energy Conservation Sustainable Building Code (ECSBC), the National Building Code (NBC), and various Green Building Rating Systems (GBRS), to assess their contributions to climate resilience. Through a structured evaluation, this study identifies the strengths, gaps, and synergies across existing standards, with a particular focus on energy efficiency, -energy conservation, material sustainability, and the integration of passive design. Peer-reviewed studies demonstrate that enforcement of these policies reduces energy consumption by up to 32% in commercial buildings and 20–30% in residential buildings. This research underscores the imperative of shifting from design compliance to performance-oriented regulation, bolstered by post-construction assessments and enhanced enforcement capabilities within Urban Local Bodies (ULBs), while also advocating for the alignment of mandatory standards with voluntary rating systems and the incorporation of climate resilience metrics to guarantee that buildings are efficient, accountable, and capable of adapting to future risks. Research highlights the need to develop a web-based platform for evaluating the performance of green-rated buildings. This platform could facilitate better communication and collaboration among stakeholders, ensuring that best practices are shared and implemented effectively.
{"title":"Building climate-resilient Indian cities through regulatory and green rating frameworks","authors":"Rohit Thakur , Anil Kumar","doi":"10.1016/j.enbuild.2026.117041","DOIUrl":"10.1016/j.enbuild.2026.117041","url":null,"abstract":"<div><div>Rapid urbanization and the increasing impact of climate change have amplified the need for climate-resilient strategies in Indian cities. Building codes and green rating systems play a pivotal role in shaping sustainable urban development. This article systematically analyzes major frameworks, including the Energy Conservation Sustainable Building Code (ECSBC), the National Building Code (NBC), and various Green Building Rating Systems (GBRS), to assess their contributions to climate resilience. Through a structured evaluation, this study identifies the strengths, gaps, and synergies across existing standards, with a particular focus on energy efficiency, -energy conservation, material sustainability, and the integration of passive design. Peer-reviewed studies demonstrate that enforcement of these policies reduces energy consumption by up to 32% in commercial buildings and 20–30% in residential buildings. This research underscores the imperative of shifting from design compliance to performance-oriented regulation, bolstered by post-construction assessments and enhanced enforcement capabilities within Urban Local Bodies (ULBs), while also advocating for the alignment of mandatory standards with voluntary rating systems and the incorporation of climate resilience metrics to guarantee that buildings are efficient, accountable, and capable of adapting to future risks. Research highlights the need to develop a web-based platform for evaluating the performance of green-rated buildings. This platform could facilitate better communication and collaboration among stakeholders, ensuring that best practices are shared and implemented effectively.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117041"},"PeriodicalIF":7.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014896","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 growing energy demand in modern buildings, especially those with extensive glazing, underscores the need for energy-efficient solutions. This study explores the potential of magnetron-sputtered TiN mono and multilayer thin films to reduce air conditioning costs and promote sustainable building applications. Coatings were applied to glass substrates of varying thicknesses (4 mm, 6 mm, and 8 mm) and evaluated their optical, thermal, and environmental performance under the hot-dry climate of Vellore, TamilNadu, India. Surface characterization using AFM and FESEM revealed nano-hill structures with increased surface roughness in Ti/TiN multilayers, which enhanced light scattering. UV–VIS-NIR spectroscopy demonstrated that Ti/TiN films effectively blocked ultraviolet (UV) and near-infrared (NiR) radiation while maintaining high visible light transmittance. Spectroscopic ellipsometry highlighted substrate thickness-dependent variations in optical properties. The Ti/TiN film on a 6 mm glass substrate exhibited an optimal combination for low-E applications, balancing high infrared reflectance, visible light transmittance, and low UV penetration. Simulation studies using MATLAB and Design-Builder showed a 12.92% reduction in solar heat gain and improved indoor daylight distribution. Economic analysis indicated substantial reductions in air conditioning loads and electricity costs, with a payback period of 5–7 years. Environmental analysis quantified a significant reduction in carbon emissions, with Ti/TiN film on a 4 mm glass substrate capable of mitigating up to 290 kg CO2/m2 annually. These findings highlight TiN-based coatings as a scalable and cost-effective solution for enhancing energy efficiency, thermal comfort, and sustainability in modern buildings, particularly in regions with hot climatic conditions.
{"title":"TiN based thin film coatings for energy efficient glazing: experimental and simulation insights for sustainable building applications","authors":"Sayan Atta , Joel Ashirvadam , Arun Tom Mathew , Sitaram Dash , Ariful Rahaman , Saboor Shaik , Uttamchand NarendraKumar","doi":"10.1016/j.enbuild.2026.117037","DOIUrl":"10.1016/j.enbuild.2026.117037","url":null,"abstract":"<div><div>The growing energy demand in modern buildings, especially those with extensive glazing, underscores the need for energy-efficient solutions. This study explores the potential of magnetron-sputtered TiN mono and multilayer thin films to reduce air conditioning costs and promote sustainable building applications. Coatings were applied to glass substrates of varying thicknesses (4 mm, 6 mm, and 8 mm) and evaluated their optical, thermal, and environmental performance under the hot-dry climate of Vellore, TamilNadu, India. Surface characterization using AFM and FESEM revealed nano-hill structures with increased surface roughness in Ti/TiN multilayers, which enhanced light scattering. UV–VIS-NIR spectroscopy demonstrated that Ti/TiN films effectively blocked ultraviolet (UV) and near-infrared (NiR) radiation while maintaining high visible light transmittance. Spectroscopic ellipsometry highlighted substrate thickness-dependent variations in optical properties. The Ti/TiN film on a 6 mm glass substrate exhibited an optimal combination for low-E applications, balancing high infrared reflectance, visible light transmittance, and low UV penetration. Simulation studies using MATLAB and Design-Builder showed a 12.92% reduction in solar heat gain and improved indoor daylight distribution. Economic analysis indicated substantial reductions in air conditioning loads and electricity costs, with a payback period of 5–7 years. Environmental analysis quantified a significant reduction in carbon emissions, with Ti/TiN film on a 4 mm glass substrate capable of mitigating up to 290 kg CO<sub>2</sub>/m<sup>2</sup> annually. These findings highlight TiN-based coatings as a scalable and cost-effective solution for enhancing energy efficiency, thermal comfort, and sustainability in modern buildings, particularly in regions with hot climatic conditions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117037"},"PeriodicalIF":7.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014901","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 : 2026-01-20DOI: 10.1016/j.enbuild.2026.117043
Tianzhen Hong, Han Li
Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.
{"title":"Good practices for documenting AI-based studies on energy and buildings","authors":"Tianzhen Hong, Han Li","doi":"10.1016/j.enbuild.2026.117043","DOIUrl":"10.1016/j.enbuild.2026.117043","url":null,"abstract":"<div><div>Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117043"},"PeriodicalIF":7.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014897","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 : 2026-01-20DOI: 10.1016/j.enbuild.2026.117036
Liwei Yang , Xiaoqing Gao , Zhenchao Li , Dongyu Jia
Extreme heatwaves are intensifying globally, yet observational evidence on the micro-climate effects of rooftop photovoltaics (PV) remains scarce, particularly in semi-arid regions. This study addresses this gap through a 46-day summer experimental investigation (July–August 2025) conducted in the semi-arid, valley-bound city of Lanzhou, north-west China. We compared the reference regular roof, photovoltaic roof (PV roof), and the cool roof integrated photovoltaic system (CPV roof), all employing double-glass modules, offering novel field-based insights into their thermal behavior under heatwaves. Results demonstrate a distinct diurnal asymmetry and vertical variation in cooling effects. Both PV and CPV roofs induced significant near-ground cooling during daytime (median: –0.49 to –0.77°C), with CPV being more effective. However, at heights above 1.5 m, PV roof maintained cooling while CPV caused slight warming. During nighttime, the thermal impact of both strategies was markedly reduced. Counterintuitively, CPV roof increased module operating temperatures by approximately 3°C than PV roof, indicating that the combination of a cool roof and PV modules does not constitute a linearly additive cooling benefit. All strategies reduced daytime roof surface temperature by 10–15°C. A robust micro-meteorological model confirmed that solar irradiance, air temperature, and wind speed dominate module heating, with PV warming twice as fast as air—affirming its role as an artificial heat island. The CPV roof showed heightened climate sensitivity, making its performance highly weather-dependent. These insights are critical for advancing sustainable city planning in a warming world.
{"title":"Observational study on the thermal performance of photovoltaic and cool-photovoltaic roofs during heatwaves in a semi-arid city","authors":"Liwei Yang , Xiaoqing Gao , Zhenchao Li , Dongyu Jia","doi":"10.1016/j.enbuild.2026.117036","DOIUrl":"10.1016/j.enbuild.2026.117036","url":null,"abstract":"<div><div>Extreme heatwaves are intensifying globally, yet observational evidence on the micro-climate effects of rooftop photovoltaics (PV) remains scarce, particularly in semi-arid regions. This study addresses this gap through a 46-day summer experimental investigation (July–August 2025) conducted in the semi-arid, valley-bound city of Lanzhou, north-west China. We compared the reference regular roof, photovoltaic roof (PV roof), and the cool roof integrated photovoltaic system (CPV roof), all employing double-glass modules, offering novel field-based insights into their thermal behavior under heatwaves. Results demonstrate a distinct diurnal asymmetry and vertical variation in cooling effects. Both PV and CPV roofs induced significant near-ground cooling during daytime (median: –0.49 to –0.77°C), with CPV being more effective. However, at heights above 1.5 m, PV roof maintained cooling while CPV caused slight warming. During nighttime, the thermal impact of both strategies was markedly reduced. Counterintuitively, CPV roof increased module operating temperatures by approximately 3°C than PV roof, indicating that the combination of a cool roof and PV modules does not constitute a linearly additive cooling benefit. All strategies reduced daytime roof surface temperature by 10–15°C. A robust micro-meteorological model confirmed that solar irradiance, air temperature, and wind speed dominate module heating, with PV warming twice as fast as air—affirming its role as an artificial heat island. The CPV roof showed heightened climate sensitivity, making its performance highly weather-dependent. These insights are critical for advancing sustainable city planning in a warming world.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117036"},"PeriodicalIF":7.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014912","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 : 2026-01-20DOI: 10.1016/j.enbuild.2026.117034
Minglu Qu, Junhan Chen, Yusen Bai, Jiajie Chen
Solar energy, as a renewable energy source, offers significant potential in the field of building heating. However, the intermittency and misalignment with grid demand periods limit its effective utilization in building heating applications. Whereas prior investigations have examined either time-of-use (TOU) electricity tariffs or energy forecasting as standalone problems, a research gap persists in synergistically integrating day-ahead forecasts with real-time price signals to co-optimize the operation of integrated photovoltaic-thermal heat pump (PV/T-HP) systems with energy storage. To address this gap, this study proposes a photovoltaic-thermal dual-source heat pump with electricity energy storage (PV/T-DSHP-EES) system, optimized through TOU pricing-based charging and discharging strategies. Three operational strategies, i.e., self-consumption maximization (SCM) strategy, TOU and day-ahead forecasting TOU (DA-TOU), are developed and simulated using TRNSYS and MATLAB for an office building in Shanghai. Results indicate that DA-TOU strategy achieves the lowest comprehensive cost (considering both operational and environmental treatment costs) in both daily (1.31 CNY) and monthly (97.39 CNY) winter simulations, demonstrating its superiority in balancing economic and environmental performance. Furthermore, an enhanced particle swarm optimization (PSO) algorithm, improved to avoid local optima and enhance global search capability, is applied to refine the DA-TOU strategy. This optimization reduced the total grid electricity supplementation by 9.4% to 3.10 kWh and the comprehensive cost by 8.0% to 3.33 CNY. The proposed system and optimized control framework provide a replicable methodology for enhancing the economic and environmental performance of building-integrated renewable energy systems, offering a viable pathway for low-carbon heating in urban environments.
{"title":"Optimization of a photovoltaic-thermal-dual-source heat pump system using day-ahead forecasting and time-of-use pricing","authors":"Minglu Qu, Junhan Chen, Yusen Bai, Jiajie Chen","doi":"10.1016/j.enbuild.2026.117034","DOIUrl":"10.1016/j.enbuild.2026.117034","url":null,"abstract":"<div><div>Solar energy, as a renewable energy source, offers significant potential in the field of building heating. However, the intermittency and misalignment with grid demand periods limit its effective utilization in building heating applications. Whereas prior investigations have examined either time-of-use (TOU) electricity tariffs or energy forecasting as standalone problems, a research gap persists in synergistically integrating day-ahead forecasts with real-time price signals to co-optimize the operation of integrated photovoltaic-thermal heat pump (PV/T-HP) systems with energy storage. To address this gap, this study proposes a photovoltaic-thermal dual-source heat pump with electricity energy storage (PV/T-DSHP-EES) system, optimized through TOU pricing-based charging and discharging strategies. Three operational strategies, i.e., self-consumption maximization (SCM) strategy, TOU and day-ahead forecasting TOU (DA-TOU), are developed and simulated using TRNSYS and MATLAB for an office building in Shanghai. Results indicate that DA-TOU strategy achieves the lowest comprehensive cost (considering both operational and environmental treatment costs) in both daily (1.31 CNY) and monthly (97.39 CNY) winter simulations, demonstrating its superiority in balancing economic and environmental performance. Furthermore, an enhanced particle swarm optimization (PSO) algorithm, improved to avoid local optima and enhance global search capability, is applied to refine the DA-TOU strategy. This optimization reduced the total grid electricity supplementation by 9.4% to 3.10 kWh and the comprehensive cost by 8.0% to 3.33 CNY. The proposed system and optimized control framework provide a replicable methodology for enhancing the economic and environmental performance of building-integrated renewable energy systems, offering a viable pathway for low-carbon heating in urban environments.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117034"},"PeriodicalIF":7.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014910","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 : 2026-01-19DOI: 10.1016/j.enbuild.2026.117030
Hussein Elehwany , Andre Markus , Burak Gunay , Mohamed Ouf , Nunzio Cotrufo , Jean-Simon Venne , Junfeng Wen
Occupant behaviour (OB) centric controls have significant potential in advancing next-generation HVAC systems. Many OB-centric control studies solicit feedback from occupants to tackle the thermal preference learning problem. Behaviour nudging was also implemented in various systems to influence occupant behaviour to be more energy efficient. This study addresses the gap of using behaviour nudging and unsolicited occupant thermostat overrides to learn their thermal preferences. A multi-armed bandit (MAB) reinforcement learning (RL) was used to learn occupant thermal preferences from their thermostat interactions. The reward signal of the algorithm was designed to reward energy savings and penalize discomfort. The occupants were continuously nudged by slowly reducing the zone setpoint during the heating season, to encourage them to override the thermostats. The algorithm was implemented in two zones with multiple occupants in an academic facility in Ottawa, Canada, achieving energy savings of up to 12.7% compared to static setpoints.
{"title":"Adaptive thermostat preference learning using behaviour nudging and multi-armed bandits: A field implementation","authors":"Hussein Elehwany , Andre Markus , Burak Gunay , Mohamed Ouf , Nunzio Cotrufo , Jean-Simon Venne , Junfeng Wen","doi":"10.1016/j.enbuild.2026.117030","DOIUrl":"10.1016/j.enbuild.2026.117030","url":null,"abstract":"<div><div>Occupant behaviour (OB) centric controls have significant potential in advancing next-generation HVAC systems. Many OB-centric control studies solicit feedback from occupants to tackle the thermal preference learning problem. Behaviour nudging was also implemented in various systems to influence occupant behaviour to be more energy efficient. This study addresses the gap of using behaviour nudging and unsolicited occupant thermostat overrides to learn their thermal preferences. A multi-armed bandit (MAB) reinforcement learning (RL) was used to learn occupant thermal preferences from their thermostat interactions. The reward signal of the algorithm was designed to reward energy savings and penalize discomfort. The occupants were continuously nudged by slowly reducing the zone setpoint during the heating season, to encourage them to override the thermostats. The algorithm was implemented in two zones with multiple occupants in an academic facility in Ottawa, Canada, achieving energy savings of up to 12.7% compared to static setpoints.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117030"},"PeriodicalIF":7.1,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001008","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 : 2026-01-19DOI: 10.1016/j.enbuild.2026.117025
Fatimah Faiza Farrukh, Manar Amayri
Smart building automation helps by enhancing occupant comfort, cost-efficiency and reduces energy waste. However, correctly utilizing these benefits depends on accurately understanding occupant behavior, such as occupancy patterns, activities, and appliance usage. But to collect such sensitive data raises serious privacy concerns, such as data leakages and breaches. In addition, deep learning models often require large amounts of data and high computational resources, leading to increased bandwidth usage and processing delays that make sensor-based systems inefficient. To address these challenges, we propose a federated neuro-symbolic rule learning framework that combines privacy-preserving federated learning with explainable symbolic rule generation. The generated rules are lightweight and edge-deployable, and make our framework the first federated neuro-symbolic approach designed for smart building operations. Our method allows clients to collaboratively train a Transformer-based rule generator via reinforcement learning and supervised fine-tuning without sharing raw data. Results showed that our model outperformed both deep and rule-based baselines, achieving up to 25–45% higher test accuracy, while being 2–3 × smaller and running in half the time as rule based models such as Apriori and FP-Growth, and about 200 × faster and 60 × smaller than deep learning baselines. The model also demonstrated strong generalizability by achieving 94.3% test accuracy on unseen data compared to an average of 74.6% for traditional and deep baselines — reflecting approximately 20% improvement in generalization performance on unseen data. The code for the proposed model is available at https://github.com/ffaizaf/FedNSRL
{"title":"Federated neuro-symbolic rule learning for lightweight smart building operations","authors":"Fatimah Faiza Farrukh, Manar Amayri","doi":"10.1016/j.enbuild.2026.117025","DOIUrl":"10.1016/j.enbuild.2026.117025","url":null,"abstract":"<div><div>Smart building automation helps by enhancing occupant comfort, cost-efficiency and reduces energy waste. However, correctly utilizing these benefits depends on accurately understanding occupant behavior, such as occupancy patterns, activities, and appliance usage. But to collect such sensitive data raises serious privacy concerns, such as data leakages and breaches. In addition, deep learning models often require large amounts of data and high computational resources, leading to increased bandwidth usage and processing delays that make sensor-based systems inefficient. To address these challenges, we propose a federated neuro-symbolic rule learning framework that combines privacy-preserving federated learning with explainable symbolic rule generation. The generated rules are lightweight and edge-deployable, and make our framework the first federated neuro-symbolic approach designed for smart building operations. Our method allows clients to collaboratively train a Transformer-based rule generator via reinforcement learning and supervised fine-tuning without sharing raw data. Results showed that our model outperformed both deep and rule-based baselines, achieving up to 25–45% higher test accuracy, while being 2–3 × smaller and running in half the time as rule based models such as Apriori and FP-Growth, and about 200 × faster and 60 × smaller than deep learning baselines. The model also demonstrated strong generalizability by achieving 94.3% test accuracy on unseen data compared to an average of 74.6% for traditional and deep baselines — reflecting approximately 20% improvement in generalization performance on unseen data. The code for the proposed model is available at <span><span>https://github.com/ffaizaf/FedNSRL</span><svg><path></path></svg></span></div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117025"},"PeriodicalIF":7.1,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000927","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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