Energy and Buildings最新文献

{"title":"Enhancing building cooling efficiency with water-active PCM panels and displacement ventilation in hot climates","authors":"Osama Sabah Almtuly ,&nbsp;Mazlan Abdul Wahid ,&nbsp;Hasanen M. Hussen ,&nbsp;Mohd Ibthisham Ardani ,&nbsp;Keng Yinn Wong ,&nbsp;Ihab Hasan Hatif","doi":"10.1016/j.enbuild.2025.115688","DOIUrl":"10.1016/j.enbuild.2025.115688","url":null,"abstract":"<div><div>Buildings in extremely hot climates have high energy demands and carbon emissions due to intensive cooling requirements, emphasizing the need for innovative, energy-efficient cooling solutions. This study introduces and evaluates the performance of a novel cooling system that integrates phase change material (PCM) into water-active ceiling panels combined with displacement ventilation (DV). The PCM used in this study is sourced from waste petroleum products, making it abundant and cost-effective. Using full-scale experiments and CFD simulations, this research assesses the system’s impact on cooling energy consumption, thermal comfort, and indoor air quality, comparing it to conventional cooling systems. The results show that the novel system reduces indoor air temperature peaks by up to 3.5 °C, enhances thermal comfort, and lowers cooling energy consumption, achieving monthly energy savings of up to 32 %. The PCM ceiling panels also reduce peak power usage and overall energy demands through efficient heat storage and re-solidification cycles, enabling shorter cooling operating times. Furthermore, the combined PCM-DV system delivers stable, uniform indoor temperatures, improving occupant comfort and enhancing indoor air quality. This study demonstrates the potential of PCM-enhanced cooling systems in extremely hot climates and provides actionable insights for energy-efficient building strategies in arid regions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115688"},"PeriodicalIF":6.6,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision 2035: A forecasting framework for household final energy consumption in Türkiye","authors":"Mehmet Melikoglu","doi":"10.1016/j.enbuild.2025.115689","DOIUrl":"10.1016/j.enbuild.2025.115689","url":null,"abstract":"<div><div>This study develops per capita-based forecasting models to estimate household final energy consumption (FEC) in Türkiye from 2024 to 2035. The models demonstrate strong predictive accuracy, aligning with historical data, the 2022 National Energy Plan (NEP) targets, and key statistical metrics. The most reliable scenario assumes per capita household FEC will increase at a rate similar to Spain’s historical trend (2003–2023), projecting FEC to reach 1,110,000 TJ in 2025, 1,200,000 TJ in 2030, and 1,285,000 TJ in 2035. The 2035 forecast achieves a 99.9% match with the official NEP target, underscoring its robustness. The findings indicate a growing demand for household energy, driven by population growth and economic expansion, with no expected supply–demand imbalances. Urbanization, lifestyle changes, and housing conditions significantly influence energy use. This methodology offers a reliable foundation for energy policy, aiding researchers and policymakers in designing sustainable strategies to enhance energy efficiency and ensure long-term resource management in Türkiye.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115689"},"PeriodicalIF":6.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global investigation of pedestrian-level cooling and energy-saving potentials of green and cool roofs in 43 megacities","authors":"Siqi Jia ,&nbsp;Qihao Weng ,&nbsp;Cheolhee Yoo ,&nbsp;James A. Voogt","doi":"10.1016/j.enbuild.2025.115671","DOIUrl":"10.1016/j.enbuild.2025.115671","url":null,"abstract":"<div><div>Green roofs and cool roofs are emerging as two potential solutions to combat the negative impacts of urban warming in the context of climate change. However, the existing body of research has not clearly established the connection between the local built environment and the effectiveness of these solutions. Moreover, a lack of standardized methodologies for integrating micro-scale climatic data has impeded the precision of modeling endeavors. In light of these knowledge gaps, an extensive study was conducted across 43 megacities to evaluate the impact of green and cool roofs on reducing urban temperatures and building energy consumption. A novel integrated approach, combining a micro-level computational fluid dynamics (CFD) model and a building energy simulation method, was used. The results reveal that both cool and green roofs moderately cool pedestrian areas, with green roofs slightly outperforming cool roofs, reducing temperatures by an average of 0.10 °C. Delhi reported the highest cooling effect from green roofs at 0.80 °C, while Beijing recorded the top cooling performance from cool roofs at 0.23 °C. Cool roofs showed significant cooling energy savings, from 5.4 to 63.8 kWh/m²/year, particularly in sun-drenched cities like Bangalore, Dhaka, and Ahmedabad, albeit their inability to save heating energy in higher latitudes. Conversely, green roofs provided consistent energy savings, typically from 1.1 to 7.3 kWh/m²/year, with Dhaka exhibiting the highest energy-saving amount.<!--> <!-->Additionally, the study also identified that<!--> <!-->urban morphology influences the effectiveness of these strategies. The cooling effect becomes less noticeable with increasing building height, and open layouts are more conducive to roof-level strategies. The findings from this study will help optimize the implementation of these strategies in different climates and built environments, contributing to efforts to mitigate global climate change and enhance urban livability.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115671"},"PeriodicalIF":6.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel systematic heat integration and heat recovery approach for reactivating abandoned mines to meet energy demand of greenhouses-application of dynamic pinch analysis","authors":"Hosein Faramarzpour ,&nbsp;Soroush Entezari ,&nbsp;Mikhail Sorin ,&nbsp;Michel Grégoire","doi":"10.1016/j.enbuild.2025.115678","DOIUrl":"10.1016/j.enbuild.2025.115678","url":null,"abstract":"<div><div>Designing an optimum and efficient energy system for a greenhouse in cold climate conditions, such as Canada, is a very challenging task, and is even more sophisticated when different sources of energies (solar, geothermal, etc.) should be integrated into the energy system. This study, for the first time, is proposing a systematic heat integration approach, based on Dynamic Pinch Analysis, to improve the efficiency of the energy system of a greenhouse through taking advantage of heat recovery from waste energies (grey water and air ventilation). Also, it proposed a novel methodology to integrate a solar assisted geothermal heat pump system into a greenhouse to eliminate fossil fuel consumption. Following the evaluation of the geothermal energy potential of an open pit lake of an abandoned mine (King Beaver Mine), a mathematical energy model was developed to calculate the energy demand of the case study greenhouse in Quebec, Canada. To reduce the calculation time, two unsupervised machine learning techniques (K-Means and K-medoids) were used to identify the typical days (TDs). For each typical day and each time slice (1 hr), composite curves (CCs) were plotted. These CCs enabled energy targeting by maximizing heat recovery and facilitating the design of an optimal heat exchanger network (HEN). A techno-economic analysis was then conducted to determine the optimal HEN configuration among the scenarios, ensuring efficient placement of heat exchangers to maximize energy efficiency and cost savings for the greenhouse climate control system. It is shown that by taking advantage of heat recovery from waste energy 38 percent energy saving is possible. Calculations indicate that using a properly sized thermal energy storage unit could reduce the condenser size of the heat pump by over 40 percent.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115678"},"PeriodicalIF":6.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection and integration strategies of PCMs in traditional bricks for thermal comfort and energy efficiency: A comprehensive review","authors":"N. Ruiz-Marín","doi":"10.1016/j.enbuild.2025.115663","DOIUrl":"10.1016/j.enbuild.2025.115663","url":null,"abstract":"<div><div>Traditional bricks, while being cost-effective and durable construction materials, exhibit limited thermal performance under extreme weather conditions, which can lead to uncomfortable indoor environments and increased energy consumption for heating and cooling. To address this issue, the integration of phase change materials (PCMs) into bricks has emerged as an area of interest in sustainable construction. PCMs have the ability to store and release large amounts of latent heat during phase transitions, acting as a thermal buffer that regulates indoor temperature. However, a comprehensive review on how PCM integration impacts thermal performance, energy savings, environmental effects, and costs—particularly in bricks—has not yet been published. This review examines strategies for the selection and integration of PCMs in traditional bricks, analyzing the different types of PCMs, their properties, and the most common integration techniques. The influence of brick design on thermal performance is discussed, along with the importance of building orientation to maximize system efficiency. Several studies demonstrate that the integration of PCMs into bricks significantly reduces indoor temperature fluctuations, heat flow, and cooling demand, thereby improving thermal comfort and energy efficiency in buildings. However, challenges remain, such as improving thermal conductivity, reducing costs, and ensuring the safety of PCMs. Future research is needed to optimize integration techniques, develop PCMs with enhanced properties, and establish safety guidelines, paving the way for the widespread adoption of this technology in the construction of more sustainable buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115663"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outdoor thermal benchmarks for the elderly and its application to the outdoor spaces design of nursing homes in Shanghai","authors":"Pengfei Zhou ,&nbsp;Dexuan Song ,&nbsp;Chi Zhang","doi":"10.1016/j.enbuild.2025.115654","DOIUrl":"10.1016/j.enbuild.2025.115654","url":null,"abstract":"<div><div>With China’s increasing aging, many older people in cities choose to live in nursing homes.<!--> <!-->However, previous research on nursing homes has concentrated mainly on the study of elderly-friendly design and indoor thermal comfort, paying little attention to the elderly’s thermal comfort in outdoor spaces.<!--> <!-->This study investigated older people’s thermal perceptions in six typical outdoor spaces at Q nursing home in Shanghai, aiming to establish a healthy and comfortable outdoor thermal environment for older people in nursing homes. The investigation utilized physical measurements, physiological<!--> <!-->measurements, and questionnaires to explore the correlations between outdoor thermal perceptions, thermal environment factors, and space enclosing degree. The Physiological Equivalent Temperature (PET) was chosen to assess the elderly’s outdoor thermal benchmarks in Shanghai. Based on the thermal benchmarks, optimal design strategies for nursing homes’ outdoor thermal environments were proposed.<!--> <!-->Results<!--> <!-->demonstrated that: 1) The elderly’s outdoor thermal sensation and thermal comfort were closely related. Air temperature and wind speed are the major influences on the elderly’s thermal perceptions. 2) The neutral PET, neutral PET range, acceptable PET range, and preferred PET of the elderly in Shanghai were identified as 18.3℃, 11.24 °C-25.26℃, 9.48 °C-28.4℃<!--> <!-->and 20.9℃, respectively.<!--> <!-->3) The elderly’s outdoor thermal sensation and the mean skin temperature are closely correlated with the building space’s enclosing degree and opening direction.<!--> <!-->Semi-open spaces are the most comfortable, and open spaces have the lowest comfort. 4) Optimum design strategies<!--> <!-->were proposed based on the natural and built environments, and the results provide design strategies for the<!--> <!-->outdoor thermal environments of nursing homes in regions of hot<!--> <!-->summer and cold<!--> <!-->winter.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115654"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-depth sensitivity analysis of heating demand and overheating in Dutch terraced houses using interpretable machine learning","authors":"Alexis Cvetkov-Iliev ,&nbsp;Vasilis Soulios ,&nbsp;Luyi Xu ,&nbsp;Günsu Merin Abbas ,&nbsp;Evangelos Kyrou ,&nbsp;Lisanne Havinga ,&nbsp;Pieter Jan Hoes ,&nbsp;Roel Loonen ,&nbsp;Joaquin Vanschoren","doi":"10.1016/j.enbuild.2025.115611","DOIUrl":"10.1016/j.enbuild.2025.115611","url":null,"abstract":"<div><div>Sensitivity analyses are often performed to facilitate the design or modeling of complex building systems by focusing on the most influential parameters. However, the insights they produce are generally limited to a ranking of the parameters’ impact. Instead, many applications could benefit from more advanced insights, such as how these impacts vary across different parameter values and scenarios. In addition, sensitivity analysis results should be easily interpretable to facilitate subsequent decision making. With these goals in mind, this paper introduces a novel sensitivity analysis method based on partial dependence (PD) plots. PD plots are further combined with dictionary learning, advanced visualizations, and surrogate models to facilitate their analysis and reduce their computational cost. Using this method, the effect of 26 parameters on heating demand and overheating in Dutch terraced houses is investigated. Two surrogate models are trained on 66,000 EnergyPlus simulations to predict the annual heating demand and percentage of overheating hours with excellent precision (<span><math><mrow><mo>≈</mo><mn>3</mn></mrow></math></span>–4 % of percentage error). The benefits of our approach are demonstrated through 3 use cases: 1) a comparison of the impact and energy-overheating trade-off of insulation measures across various scenarios, 2) improving the design of parametric simulations by eliminating redundant parameter values, and 3) uncovering complex behaviors in simulation or surrogate models, to build trust in them and diagnose potential modeling or training errors. Finally, our results suggest that surrogate models can be trained on much less data (1000–3000) without compromising sensitivity analysis results.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115611"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explaining building energy efficiency prediction through architectural and engineering solutions considering environmental impacts using a hybrid model","authors":"Semi Emrah Aslay","doi":"10.1016/j.enbuild.2025.115614","DOIUrl":"10.1016/j.enbuild.2025.115614","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The aim of this study is to investigate building energy efficiency by integrating hybrid modelling approaches and interpretable models into architectural design processes and engineering solutions while considering environmental impacts. At the same time, it is aimed to achieve the most efficient building design possible. Separate analyses were carried out using a total of 6,913 data from Rutland (185) and Salford (6718) cities. The data were grouped into carbon emission information, architectural information, lighting information, personal heating information, and main heating system information to form a dataset. Light Gradient Boosting Machine (LightGBM) was preferred as the base model and Particle Swarm Optimisation (PSO) method was applied for hyperparameter optimisation. The hybrid model created in this way is called PSO-LightGBM. The optimization process was carried out using software in both R Studio and Python environments, utilizing seven different hyperparameters. Apart from the hybrid model used as a method, 2 different SHAP analyses, neural network based, and tree based, were performed to clearly explain the parameter relationships. The PSO-LightGBM hybrid model provided more successful predictions compared to the basic LightGBM model. While R&lt;sup&gt;2&lt;/sup&gt; values improved between 0.82 and 0.90 in the Rutland dataset, this value increased from 0.8687 to 0.8901 for test data and from 0.8538 to 0.9091 for training data in the Salford dataset. R&lt;sup&gt;2&lt;/sup&gt; values show an improvement of 7% in the Rutland dataset and maximum 6% in the Salford dataset. When the reduction in error rates is evaluated, it is found that the greatest improvement is in the Mean Squared Error (MSE) metric. MSE decreased by 17% in the Rutland dataset and by 4% in the Salford dataset. According to the SHAP Analysis results, CO&lt;sub&gt;2&lt;/sub&gt; emissions have the largest impact on energy consumption, while primary fuel types, number heated rooms and individual heating systems are other important parameters. While the tree-based SHAP model is more sensitive to physical parameters, the neural network model is more sensitive to indirect relationships. In both analyses, the communal heating system type has the lowest impact. In order to improve building energy efficiency, high efficiency individual boiler systems should be preferred, architectural approaches that optimise the number of heated rooms and smart heating solutions should be used, and central heating systems should be modernised. The results highlight the effectiveness of hybrid modelling approaches with SHAP analyses based on different baselines to ensure the integration of environmental impacts, architectural design processes and engineering solutions in terms of building energy efficiency. Furthermore, the findings contribute to the importance of interdisciplinary work in buildings to improve energy efficiency. Future studies can focus on the development of building energy performance prediction models","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115614"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-Site temperature and irradiance forecast tuning for improved load prediction in buildings","authors":"Amine Jarraya ,&nbsp;Tim Diller ,&nbsp;Himanshu Nagpal ,&nbsp;Anton Soppelsa ,&nbsp;Federico Trentin ,&nbsp;Gregor Henze ,&nbsp;Roberto Fedrizzi","doi":"10.1016/j.enbuild.2025.115642","DOIUrl":"10.1016/j.enbuild.2025.115642","url":null,"abstract":"<div><div>Building management systems (BMSs) with predictive control strategies rely on accurate weather forecasts to optimise heating and cooling operations. These strategies depend on precise climatic inputs to adjust system operations dynamically. Typically, weather forecast data is sourced from the internet and is generated by numerical weather prediction (NWP) models using advanced mathematical simulations. However, these models fail to account for localised nano-climatic variations, such as significant temperature and irradiance differences between the north and south sides of a building or the actual environmental conditions around the on-site sensors. These nano-climatic effects directly influence the calculation of the future thermal load of the building, which is crucial for predictive control approaches. To address this challenge, we propose a hybrid methodology that integrates NWP forecasts with local measurements from on-site sensors, improving NWP forecast accuracy. Our approach employs Inverse Distance Weighting (IDW) to interpolate NWP outputs to a specific geographical position and applies exponential smoothing for further finetuning by using historical error patterns. This methodology enhances the predictive accuracy of temperature and irradiance forecasts, achieving reductions of up to 60% to 80% in temperature errors and up to 20% to 30% in irradiance errors. Based on the finetuned weather forecast, the accuracy of building’s thermal load prediction is improved up to 86% compared to the predictions with IDW weather forecast.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115642"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is it necessary to calibrate existing decision-making results based on real option analysis during the process of climate change? A case study from Xiamen, China","authors":"Wenqiang Li ,&nbsp;Xin Jin ,&nbsp;Pei Peng ,&nbsp;Zaiyi Liao ,&nbsp;Min Wu ,&nbsp;Huahua Xu ,&nbsp;Jiashun Feng","doi":"10.1016/j.enbuild.2025.115658","DOIUrl":"10.1016/j.enbuild.2025.115658","url":null,"abstract":"<div><div>Previous studies have rarely examined how well Representative Concentration Pathways (RCP) scenarios fit with real weather data making climate change related decision-makings. This paper proposes a real option analysis (ROA) decision-making calibration method based on RCP (learning) scenarios uncertainty caused by the gap between the simulated and real weather conditions. Firstly, EnergyPlus is used to predict the energy consumption of a case building based on future climatic conditions, and the learning scenarios uncertainty is quantified. Secondly, a ROA decision-making calibration model (ROACM) is established through optimization to minimize the learning scenarios uncertainty. Finally, the ROACM is applied to a case study of an office building located in Xiamen, China, aiming to calibrate individual and sequential investment decisions for three retrofitting measures (<span><math><msub><mi>R</mi><mn>1</mn></msub></math></span>: horizontal shading, <span><math><msub><mi>R</mi><mn>2</mn></msub></math></span>: low-e windows, <span><math><msub><mi>R</mi><mn>3</mn></msub></math></span>: improving COP of chiller). The accuracy of ROACM is assessed through a sensitivity analysis. The results indicate that the learning scenarios uncertainty leads to annual energy consumption differences of approximately 8,451 kW <span><math><mo>∙</mo></math></span>h to 17,545 kW<span><math><mo>∙</mo></math></span>h in the case study. After calibration, the optimal timing for both individual and sequential investment has advanced by at least 4 years, significantly boosting returns. The optimal individual investment <span><math><msub><mi>R</mi><mn>3</mn></msub></math></span> generated an additional return of up to $52,744, while the sequential investment <span><math><mrow><msub><mi>R</mi><mn>1</mn></msub><mo>+</mo><msub><mi>R</mi><mn>13</mn></msub></mrow></math></span> (shading first, and improving COP of chiller later), increased by $175,017. The proposed ROACM demonstrated high performance during model validation in a case study, and could also aid governments or investors in ROA-based mitigation strategies analysis and increasing the reliability of the results.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115658"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739822","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}