Pub Date : 2026-02-03DOI: 10.1016/j.enbuild.2026.117107
Thomas Olsson , Myriam Aries
Building automation systems increasingly rely on advanced sensor technologies to improve energy efficiency and occupant comfort in indoor environments. This study investigates the real-world performance of a commercially available optical occupancy sensor with edge computing capabilities in a classroom setting. The sensor was tested under controlled conditions to evaluate its accuracy in detecting occupants based on spatial and human-related factors, including distance, posture, orientation, and clothing. Results showed that detection accuracy significantly declined with increased distance and when participants faced away from the sensor. The average detection accuracy was 61%, which is below the threshold typically required for reliable building automation. Exploratory regression analysis confirmed that proximity and orientation were the most influential factors affecting the probability of detection. The findings highlight the importance of strategic sensor placement and suggest that multi-sensor configurations may be necessary for effective coverage. Limitations include a small sample size, static testing conditions, and the lack of comparison with alternative technologies.
{"title":"Non-intrusive occupant detection in a real environment: a pilot study in an educational test bed facility","authors":"Thomas Olsson , Myriam Aries","doi":"10.1016/j.enbuild.2026.117107","DOIUrl":"10.1016/j.enbuild.2026.117107","url":null,"abstract":"<div><div>Building automation systems increasingly rely on advanced sensor technologies to improve energy efficiency and occupant comfort in indoor environments. This study investigates the real-world performance of a commercially available optical occupancy sensor with edge computing capabilities in a classroom setting. The sensor was tested under controlled conditions to evaluate its accuracy in detecting occupants based on spatial and human-related factors, including distance, posture, orientation, and clothing. Results showed that detection accuracy significantly declined with increased distance and when participants faced away from the sensor. The average detection accuracy was 61%, which is below the threshold typically required for reliable building automation. Exploratory regression analysis confirmed that proximity and orientation were the most influential factors affecting the probability of detection. The findings highlight the importance of strategic sensor placement and suggest that multi-sensor configurations may be necessary for effective coverage. Limitations include a small sample size, static testing conditions, and the lack of comparison with alternative technologies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117107"},"PeriodicalIF":7.1,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110235","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}
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.
{"title":"Designing glass materials for renewable-energy production through building integrated photovoltaics (BIPV) −A computational approach","authors":"Aqsa Aleem , Uzma Habib , Waqas Salman , 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 SiO<sub>2</sub> as the base composition, the effects of Na<sub>2</sub>O, CaO, and Al<sub>2</sub>O<sub>3</sub> additives (5–50 %) on glass transition temperature (Tg), thermal conductivity, and PV cell efficiency were systematically investigated. MD simulations reveal that Na<sub>2</sub>O acts as a strong network modifier, significantly reducing Tg at low concentrations, whereas CaO enhances mechanical stability and Al<sub>2</sub>O<sub>3</sub> 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":"2026-02-03","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}
Pub Date : 2026-02-02DOI: 10.1016/j.enbuild.2026.117101
Bo Peng, Baolin Cui, Xin Ma, Che Liu
Effectively identifying residential flexible resources is critical for carbon–neutral power systems. To address the complexity of user behavior, this paper proposes an integrated framework combining Long Short-Term Memory (LSTM) networks and Weighted Gene Co-expression Network Analysis (WGCNA). By encoding daily load profiles as dynamic gene expressions, the method constructs a topological overlap matrix to identify co-varying load patterns. A two-level assessment model based on Customer Directrix Load (CDL) is then established to quantify theoretical demand response potential. A case study on 200 households identify five typical usage patterns. The proposed method achieves a Davies-Bouldin Index of 2.24, which is superior to K-means at 2.84 and AE + K-means at 2.45. Furthermore, it improves cluster boundary separability by approximately 43%. Evaluation results quantify flexibility disparities, identifying Pattern C2, representing active evening users, as the highest potential group with a score of 0.58, and Pattern C5 as the lowest potential group with a score of 0.40. These findings provide actionable data support for aggregators to formulate differentiated flexibility allocation strategies.
{"title":"Data-Driven assessment of demand response potential of residential loads based on LSTM-WGCNA and Customer directrix load","authors":"Bo Peng, Baolin Cui, Xin Ma, Che Liu","doi":"10.1016/j.enbuild.2026.117101","DOIUrl":"10.1016/j.enbuild.2026.117101","url":null,"abstract":"<div><div>Effectively identifying residential flexible resources is critical for carbon–neutral power systems. To address the complexity of user behavior, this paper proposes an integrated framework combining Long Short-Term Memory (LSTM) networks and Weighted Gene Co-expression Network Analysis (WGCNA). By encoding daily load profiles as dynamic gene expressions, the method constructs a topological overlap matrix to identify co-varying load patterns. A two-level assessment model based on Customer Directrix Load (CDL) is then established to quantify theoretical demand response potential. A case study on 200 households identify five typical usage patterns. The proposed method achieves a Davies-Bouldin Index of 2.24, which is superior to K-means at 2.84 and AE + K-means at 2.45. Furthermore, it improves cluster boundary separability by approximately 43%. Evaluation results quantify flexibility disparities, identifying Pattern C2, representing active evening users, as the highest potential group with a score of 0.58, and Pattern C5 as the lowest potential group with a score of 0.40. These findings provide actionable data support for aggregators to formulate differentiated flexibility allocation strategies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117101"},"PeriodicalIF":7.1,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098300","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 : 2026-02-02DOI: 10.1016/j.enbuild.2026.117084
Jihyun Kang, Chaerin Kim, Albert Tonghoon Han
Urban energy consumption is unevenly distributed across neighborhoods, yet most studies in South Korea remain focused on national or metropolitan scales. This study examines neighborhood-level (dong-level) energy demand in Seoul by integrating building characteristics, socio-economic indicators, and infrastructural access. Using multivariate regression models for gas, electricity, and total energy use across 426 neighborhoods, we identify localized drivers of urban energy demand. Results show that apartments and townhouses are consistently associated with higher energy usage, while commercial and educational facilities such as cafés, restaurants, tutoring centers, and universities substantially increase electricity demand. Socio-economic variables including income, worker density, household size, and demographic dependency ratios produce varying effects, with lower consumption in some aging or dependent populations reflecting energy deprivation rather than efficiency. District heating access demonstrates a strong negative association with neighborhood gas use, underscoring the role of centralized infrastructure. These findings reveal how built environment, social structure, and energy infrastructure jointly shape intra-urban disparities in energy demand. We argue for neighborhood-scale, equity-sensitive energy governance as a complement to city-level policies.
{"title":"Neighborhood-level analysis of building use and socio-economic effects on urban energy consumption","authors":"Jihyun Kang, Chaerin Kim, Albert Tonghoon Han","doi":"10.1016/j.enbuild.2026.117084","DOIUrl":"10.1016/j.enbuild.2026.117084","url":null,"abstract":"<div><div>Urban energy consumption is unevenly distributed across neighborhoods, yet most studies in South Korea remain focused on national or metropolitan scales. This study examines neighborhood-level (<em>dong</em>-level) energy demand in Seoul by integrating building characteristics, socio-economic indicators, and infrastructural access. Using multivariate regression models for gas, electricity, and total energy use across 426 neighborhoods, we identify localized drivers of urban energy demand. Results show that apartments and townhouses are consistently associated with higher energy usage, while commercial and educational facilities such as cafés, restaurants, tutoring centers, and universities substantially increase electricity demand. Socio-economic variables including income, worker density, household size, and demographic dependency ratios produce varying effects, with lower consumption in some aging or dependent populations reflecting energy deprivation rather than efficiency. District heating access demonstrates a strong negative association with neighborhood gas use, underscoring the role of centralized infrastructure. These findings reveal how built environment, social structure, and energy infrastructure jointly shape intra-urban disparities in energy demand. We argue for neighborhood-scale, equity-sensitive energy governance as a complement to city-level policies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117084"},"PeriodicalIF":7.1,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098302","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 : 2026-02-01DOI: 10.1016/j.enbuild.2026.117074
Siyao Wang, Hongwei Yang, Ye Zhang
The building energy efficiency and outdoor thermal comfort (OTC) in residential blocks are critical factors for the economic and social benefits of cities. Several studies have proposed frameworks for the optimal design of residential blocks targeting building energy efficiency and OTC. However, these studies have paid limited attention to the trade-off between these two objectives, and have placed less focus on the integration of solar energy potential (SEP). Therefore, this paper proposes a multi-phase optimization framework: first, obtaining Pareto optimal solutions that lower building energy consumption (BEC) and higher SEP; then, simulating OTC to inform second-phase decisions and selecting the final optimal design solutions. The overarching goal of this framework is to achieve co-optimization across energy supply, energy demand, and thermal comfort within residential blocks. To implement this approach, nine building group prototypes were identified in Wuhan, China, and 1000 samples were generated using Latin hypercube sampling. With the support of XGBoost and NSGA-II, prediction models were established between the layout of the nine group types and both BEC and SEP, yielding Pareto optimal solutions. Subsequent OTC simulations were conducted to guide the selection of the final optimal design solutions. These final solutions reduced BEC by 3.77%, increased SEP by 35.99%, and improved OTC by 33.35%. Overall, the results provide a decision-making basis for promoting high-quality urban construction in other climate zones and design solutions for comfort and efficiency in residential blocks.
{"title":"Multi-phase optimization framework of residential blocks: Balancing building energy consumption, solar energy potential, and outdoor thermal comfort","authors":"Siyao Wang, Hongwei Yang, Ye Zhang","doi":"10.1016/j.enbuild.2026.117074","DOIUrl":"10.1016/j.enbuild.2026.117074","url":null,"abstract":"<div><div>The building energy efficiency and outdoor thermal comfort (OTC) in residential blocks are critical factors for the economic and social benefits of cities. Several studies have proposed frameworks for the optimal design of residential blocks targeting building energy efficiency and OTC. However, these studies have paid limited attention to the trade-off between these two objectives, and have placed less focus on the integration of solar energy potential (SEP). Therefore, this paper proposes a multi-phase optimization framework: first, obtaining Pareto optimal solutions that lower building energy consumption (BEC) and higher SEP; then, simulating OTC to inform second-phase decisions and selecting the final optimal design solutions. The overarching goal of this framework is to achieve co-optimization across energy supply, energy demand, and thermal comfort within residential blocks. To implement this approach, nine building group prototypes were identified in Wuhan, China, and 1000 samples were generated using Latin hypercube sampling.<!--> <!-->With the support of XGBoost and NSGA-II, prediction models were established between the layout of the nine group types and both BEC and SEP, yielding Pareto optimal solutions. Subsequent OTC simulations were conducted to guide the selection of the final optimal design solutions. These final solutions reduced BEC by 3.77%, increased SEP by 35.99%, and improved OTC by 33.35%. Overall, the results provide a decision-making basis for promoting high-quality urban construction in other climate zones and design solutions for comfort and efficiency in residential blocks.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117074"},"PeriodicalIF":7.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098305","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 : 2026-02-01DOI: 10.1016/j.enbuild.2026.117094
Rodrigo Fiorotti , Jussara F. Fardin , Helder R.O. Rocha , Augusto C. Rueda-Medina , Antonio M. Pantaleo , Mohammad R. Nasab , Sergio Bruno
This paper introduces a new Home Energy Management System (HEMS) strategy designed for Smart Homes that performs load scheduling and contains Photovoltaic/Thermal (PVT) generation and Electric Vehicle (EV) using two approaches: one designed for mono-directional EV charger and the other for bi-directional one, which can perform vehicle-to-grid (V2G). The problem is solved using the Non-dominated Sorting Genetic Algorithm III metaheuristic and Long Short-Term Memory to predict the PVT generation. The occupant behavior applied utilizes 3 feature parameters to define the usage profile of controllable loads and determine the periods in which the user is most adapted to using the equipment to quantify their comfort, in addition to modeling the dependency between the operation of some loads. The electrical and thermal loads are categorized into non-controllable, deferrable, and thermo-controllable. An annual case study shows an average cost reduction of 14.57% achieved by leveraging the flexibility of the bidirectional charger for similar values of emissions and user comfort. This reduction occurs by exploiting time-of-use tariffs (which lead to an average savings of 22.23%) and reducing the maximum demand (resulting in an average reduction of 21.73%). These savings are sufficient to offset the increase in battery losses and degradation costs to perform V2G. Finally, the comparison of various HEMS architectures highlights the advantages of EV adoption through V2G implementation, positioning EVs as a more competitive solution for promoting clean and affordable energy in residential buildings.
{"title":"A new approach for HEMS to optimize cost, emissions and comfort through a smart integration of V2G, load scheduling, and PVT generation","authors":"Rodrigo Fiorotti , Jussara F. Fardin , Helder R.O. Rocha , Augusto C. Rueda-Medina , Antonio M. Pantaleo , Mohammad R. Nasab , Sergio Bruno","doi":"10.1016/j.enbuild.2026.117094","DOIUrl":"10.1016/j.enbuild.2026.117094","url":null,"abstract":"<div><div>This paper introduces a new Home Energy Management System (HEMS) strategy designed for Smart Homes that performs load scheduling and contains Photovoltaic/Thermal (PVT) generation and Electric Vehicle (EV) using two approaches: one designed for mono-directional EV charger and the other for bi-directional one, which can perform vehicle-to-grid (V2G). The problem is solved using the Non-dominated Sorting Genetic Algorithm III metaheuristic and Long Short-Term Memory to predict the PVT generation. The occupant behavior applied utilizes 3 feature parameters to define the usage profile of controllable loads and determine the periods in which the user is most adapted to using the equipment to quantify their comfort, in addition to modeling the dependency between the operation of some loads. The electrical and thermal loads are categorized into non-controllable, deferrable, and thermo-controllable. An annual case study shows an average cost reduction of 14.57% achieved by leveraging the flexibility of the bidirectional charger for similar values of emissions and user comfort. This reduction occurs by exploiting time-of-use tariffs (which lead to an average savings of 22.23%) and reducing the maximum demand (resulting in an average reduction of 21.73%). These savings are sufficient to offset the increase in battery losses and degradation costs to perform V2G. Finally, the comparison of various HEMS architectures highlights the advantages of EV adoption through V2G implementation, positioning EVs as a more competitive solution for promoting clean and affordable energy in residential buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117094"},"PeriodicalIF":7.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098304","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 : 2026-02-01DOI: 10.1016/j.enbuild.2026.117098
Shen Xu , Yichen Dong , Hongxin Guo , Zhen Yu , Bo Pan , Congyue Qi , Gaomei Li
Retrofitting building envelopes in the era of existing stock is an effective pathway to achieving carbon neutrality goals, requiring a comprehensive consideration of carbon reduction benefits and economic benefits. However, existing research focuses on the individual buildings and has not yet established a comprehensive assessment method at the block scale. This study aims to propose a multi-objective framework to assess the operational carbon reduction and economic benefits of retrofitting building envelopes in old residential blocks. First, this study employs architectural typology and cluster method to categorize 102 real cases of old residential blocks in Wuhan into three fundamental categories along with twelve derived variants. Second, under both single-component and multi-component retrofit scenarios, 2472 renovation schemes are generated. Then, sensitivity and variance analyses within the framework identify the key retrofit components and elements under each objective. Finally, the optimal renovation scheme sets for 3 types of old residential blocks are obtained, and the multi-level technical lists of envelope components-renovation materials-thermal parameters are formed. The proposed scheme sets and multi-level technical lists demonstrated good robustness after undergoing uncertainty analysis and comprehensive verification. The results indicated that multi-component retrofits enhance the carbon reduction effect by 9.25 to 23.95%, compared to single-component approaches. The heat transfer coefficient of external walls primarily affects the carbon reduction effects, while the heat transfer coefficient of external windows and the solar heat gain coefficient primarily affect economic benefits. In terms of comprehensive benefits, the heat transfer coefficient of exterior windows and that of exterior walls collectively serve as decisive factors. The technical framework proposed in this study enables policymakers and practitioners to rapidly assess the effectiveness of schemes and identify renovation priorities in the early stage of renovation. It provides a scalable technical pathway for decarbonizing old residential blocks worldwide, thereby supporting energy efficiency, emissions cuts, and sustainability within the building sector.
{"title":"Low-carbon renovation of old residential blocks: An envelope-focused assessment framework for carbon reduction effects and economic benefits","authors":"Shen Xu , Yichen Dong , Hongxin Guo , Zhen Yu , Bo Pan , Congyue Qi , Gaomei Li","doi":"10.1016/j.enbuild.2026.117098","DOIUrl":"10.1016/j.enbuild.2026.117098","url":null,"abstract":"<div><div>Retrofitting building envelopes in the era of existing stock is an effective pathway to achieving carbon neutrality goals, requiring a comprehensive consideration of carbon reduction benefits and economic benefits. However, existing research focuses on the individual buildings and has not yet established a comprehensive assessment method at the block scale. This study aims to propose a multi-objective framework to assess the operational carbon reduction and economic benefits of retrofitting building envelopes in old residential blocks. First, this study employs architectural typology and cluster method to categorize 102 real cases of old residential blocks in Wuhan into three fundamental categories along with twelve derived variants. Second, under both single-component and multi-component retrofit scenarios, 2472 renovation schemes are generated. Then, sensitivity and variance analyses within the framework identify the key retrofit components and elements under each objective. Finally, the optimal renovation scheme sets for 3 types of old residential blocks are obtained, and the multi-level technical lists of envelope components-renovation materials-thermal parameters are formed. The proposed scheme sets and multi-level technical lists demonstrated good robustness after undergoing uncertainty analysis and comprehensive verification. The results indicated that multi-component retrofits enhance the carbon reduction effect by 9.25 to 23.95%, compared to single-component approaches. The heat transfer coefficient of external walls primarily affects the carbon reduction effects, while the heat transfer coefficient of external windows and the solar heat gain coefficient primarily affect economic benefits. In terms of comprehensive benefits, the heat transfer coefficient of exterior windows and that of exterior walls collectively serve as decisive factors. The technical framework proposed in this study enables policymakers and practitioners to rapidly assess the effectiveness of schemes and identify renovation priorities in the early stage of renovation. It provides a scalable technical pathway for decarbonizing old residential blocks worldwide, thereby supporting energy efficiency, emissions cuts, and sustainability within the building sector.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117098"},"PeriodicalIF":7.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098303","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 : 2026-01-31DOI: 10.1016/j.enbuild.2026.117075
Justinas Smertinas , Nicolaj Hans Nielsen , Matthias Y.C. Van Hove , Peder Bacher , Henrik Madsen
Energy Signature (ES) models are widely used in building energy performance assessment due to their simplicity, scalability, and physical interpretability. Nevertheless, conventional ES formulations are deterministic and provide limited insight into parameter uncertainty, constraining their value for robust performance evaluation and decision-making under real-world data variability.
This work addresses this gap by investigating how scalable Bayesian statistical inference can be systematically integrated into the ES framework to enable probabilistic, scalable assessments of building thermal performance at both individual building and building stock level. The research examines whether Bayesian ES models improve predictive performance while providing transparent uncertainty quantification for key thermal parameters, such as the effective heat transfer coefficient, solar gain, and wind infiltration.
A scalable Bayesian modelling framework is developed and applied to smart-meter data from 2788 Danish single-family houses. Three model variants are formulated and compared: a baseline ES model, an auto-regressive ES model (ARX-ES) capturing thermal inertia, and an auto-regressive moving average ES model (ARMAX-ES) approximating stochastic grey-box dynamics. The models estimate the effective heat transfer coefficients, solar gains, and wind infiltration, yielding full posterior distributions to reflect parameter uncertainty.
Results show that increased model complexity enhances one-step-ahead predictive performance, with the ARMAX-ES model achieving a median Bayesian R² of 0.94 across the building stock. At the single-building level, the yearly energy demand is estimated with credibility intervals within ± 1%, showcasing more robust diagnostics than deterministic methods.
Overall, the proposed Bayesian ES framework enhances robustness and interpretability in building energy performance assessment, offering a scalable tool to complement energy certification, investment prioritisation, demand forecasting and data-driven energy planning.
{"title":"Estimation of building energy demand characteristics using bayesian statistics and energy signature models","authors":"Justinas Smertinas , Nicolaj Hans Nielsen , Matthias Y.C. Van Hove , Peder Bacher , Henrik Madsen","doi":"10.1016/j.enbuild.2026.117075","DOIUrl":"10.1016/j.enbuild.2026.117075","url":null,"abstract":"<div><div>Energy Signature (ES) models are widely used in building energy performance assessment due to their simplicity, scalability, and physical interpretability. Nevertheless, conventional ES formulations are deterministic and provide limited insight into parameter uncertainty, constraining their value for robust performance evaluation and decision-making under real-world data variability.</div><div>This work addresses this gap by investigating how scalable Bayesian statistical inference can be systematically integrated into the ES framework to enable probabilistic, scalable assessments of building thermal performance at both individual building and building stock level. The research examines whether Bayesian ES models improve predictive performance while providing transparent uncertainty quantification for key thermal parameters, such as the effective heat transfer coefficient, solar gain, and wind infiltration.</div><div>A scalable Bayesian modelling framework is developed and applied to smart-meter data from 2788 Danish single-family houses. Three model variants are formulated and compared: a baseline ES model, an auto-regressive ES model (ARX-ES) capturing thermal inertia, and an auto-regressive moving average ES model (ARMAX-ES) approximating stochastic grey-box dynamics. The models estimate the effective heat transfer coefficients, solar gains, and wind infiltration, yielding full posterior distributions to reflect parameter uncertainty.</div><div>Results show that increased model complexity enhances one-step-ahead predictive performance, with the ARMAX-ES model achieving a median Bayesian R² of 0.94 across the building stock. At the single-building level, the yearly energy demand is estimated with credibility intervals within ± 1%, showcasing more robust diagnostics than deterministic methods.</div><div>Overall, the proposed Bayesian ES framework enhances robustness and interpretability in building energy performance assessment, offering a scalable tool to complement energy certification, investment prioritisation, demand forecasting and data-driven energy planning.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117075"},"PeriodicalIF":7.1,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095870","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 : 2026-01-31DOI: 10.1016/j.enbuild.2026.117083
Meher Saketh Gandharapu , Anudeep Katepalli , Anton Harfmann , Mathias Bonmarin , John Krupczak , Donglu Shi
A novel building utility heating system is developed based on solar harvesting through multiple transparent photothermal (PT) panels arranged in a tunnel structure. Sunlight is collected by a rooftop solar dome and guided into a Photothermal Solar Tunnel (PTST), which achieves efficient conversion of solar radiation into thermal energy for passive heating. The PT panels are fabricated from thin films of Fe3O4@Cu2-xS plasmonic nanoparticles, which, due to localized surface plasmon resonance (LSPR), exhibit strong absorption in the UV and IR regions while maintaining minimal absorption in the visible band. This unique optical property enables high average visible transmittance (AVT), allowing sunlight to penetrate sequential panels within the PTST while the system functions as a transparent heat radiator. Controlled experiments under diverse environmental conditions, including extreme subzero temperatures, demonstrate the PTST’s strong heating capability: interior tunnel surfaces reached above 30.9°C from ambient temperatures of −7°C with only minimal insulation. These results establish PTST as a scalable, cost-effective, and off-grid technology for sustainable building heating. By eliminating the need for complex solar system integration or bulky components, this approach opens a new pathway for passive solar energy utilization, advancing energy-neutral and environmentally resilient solutions for next-generation buildings.
{"title":"Photothermal heating and solar harvesting through multiple transparent Fe3O4@Cu2-xS thin films with a solar dome","authors":"Meher Saketh Gandharapu , Anudeep Katepalli , Anton Harfmann , Mathias Bonmarin , John Krupczak , Donglu Shi","doi":"10.1016/j.enbuild.2026.117083","DOIUrl":"10.1016/j.enbuild.2026.117083","url":null,"abstract":"<div><div>A novel building utility heating system is developed based on solar harvesting through multiple transparent photothermal (PT) panels arranged in a tunnel structure. Sunlight is collected by a rooftop solar dome and guided into a Photothermal Solar Tunnel (PTST), which achieves efficient conversion of solar radiation into thermal energy for passive heating. The PT panels are fabricated from thin films of Fe<sub>3</sub>O<sub>4</sub>@Cu<sub>2-x</sub>S plasmonic nanoparticles, which, due to localized surface plasmon resonance (LSPR), exhibit strong absorption in the UV and IR regions while maintaining minimal absorption in the visible band. This unique optical property enables high average visible transmittance (AVT), allowing sunlight to penetrate sequential panels within the PTST while the system functions as a transparent heat radiator. Controlled experiments under diverse environmental conditions, including extreme subzero temperatures, demonstrate the PTST’s strong heating capability: interior tunnel surfaces reached above 30.9°C from ambient temperatures of −7°C with only minimal insulation. These results establish PTST as a scalable, cost-effective, and off-grid technology for sustainable building heating. By eliminating the need for complex solar system integration or bulky components, this approach opens a new pathway for passive solar energy utilization, advancing energy-neutral and environmentally resilient solutions for next-generation buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"356 ","pages":"Article 117083"},"PeriodicalIF":7.1,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095871","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 : 2026-01-31DOI: 10.1016/j.enbuild.2026.117062
Patrick X.W. Zou, Haoze Li
{"title":"Optimization of life-cycle carbon emission, global cost and thermal comfort of residential building envelops in cold regions in China","authors":"Patrick X.W. Zou, Haoze Li","doi":"10.1016/j.enbuild.2026.117062","DOIUrl":"https://doi.org/10.1016/j.enbuild.2026.117062","url":null,"abstract":"","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"44 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089291","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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