Advances in Applied Energy最新文献

Prediction-based control of energy storage systems using dynamic accuracy weighting 基于动态精度加权的储能系统预测控制

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-29 DOI: 10.1016/j.adapen.2025.100246

Xiao Wang , Xue Liu , Xuyuan Kang , Fu Xiao , Da Yan

{"title":"Prediction-based control of energy storage systems using dynamic accuracy weighting","authors":"Xiao Wang , Xue Liu , Xuyuan Kang , Fu Xiao , Da Yan","doi":"10.1016/j.adapen.2025.100246","DOIUrl":"10.1016/j.adapen.2025.100246","url":null,"abstract":"<div><div>Integrating domain knowledge into artificial intelligence models is increasingly recognized as essential for improving energy storage system control based on load predictions. Commonly used accuracy metrics for load prediction models, such as mean absolute percentage error, coefficient of variation of mean absolute error, and coefficient of variation of root mean squared error, are not monotonically correlated with final control performance; in other words, the model with the highest prediction accuracy does not necessarily yield optimal control outcomes. This study introduces a dynamically weighted error metric, which incorporates the attributes of energy storage systems and the temporal dynamics of prediction-based control by leveraging domain knowledge from heating, ventilation, and air conditioning systems. The proposed dynamically weighted error metric enhanced the selection of load prediction models, and these models reduced the operating cost of six energy storage systems by up to 6.5 % compared to those using traditional prediction accuracy metrics. The scalability of dynamically weighted error metric was further validated across 10 energy storage capacities and 18 Time-of-Use tariffs in the six building cases, achieving 93.9 %–97.2 % of the ideal cost reductions and outperforming traditional metrics (86.4 %–95.4 %). The applicability of dynamically weighted error metric to common energy storage systems is discussed and confirmed. Additionally, a web-based tool was developed to facilitate dynamically weighted error calculation in practical applications. This study demonstrates that incorporating domain knowledge through dynamic accuracy weighting evidently enhances the whole-process performance of artificial intelligence in energy storage system control.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100246"},"PeriodicalIF":13.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rethinking the atmospheric downward longwave radiation: A black-gray body model for accurate estimation 对大气向下长波辐射的再思考：一种精确估计的黑灰体模型

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-18 DOI: 10.1016/j.adapen.2025.100244

Lanxin Li , Xianze Ao , Qiangyan Hao , Meiling Liu , Xiansheng Li , Kegui Lu , Chongwen Zou , Bin Zhao , Gang Pei

{"title":"Rethinking the atmospheric downward longwave radiation: A black-gray body model for accurate estimation","authors":"Lanxin Li , Xianze Ao , Qiangyan Hao , Meiling Liu , Xiansheng Li , Kegui Lu , Chongwen Zou , Bin Zhao , Gang Pei","doi":"10.1016/j.adapen.2025.100244","DOIUrl":"10.1016/j.adapen.2025.100244","url":null,"abstract":"<div><div>Accurately estimating atmospheric downward longwave radiation is critical for applications ranging from radiative cooling to building energy efficiency. The main challenge lies in its spectral variability, which depends strongly on sky conditions such as humidity and cloud cover. In this study, we propose a Black–Gray body atmospheric radiation model that divides the infrared spectrum into three regions, treating the atmosphere as a graybody in the 8–13 μm and a blackbody outside this band. The model integrates locally measured radiative power to dynamically capture temporal and spatial variations. Validation experiments were conducted using radiative cooling processes in three Chinese cities (Hefei, Lhasa, and Haikou) under different climates and weather conditions. The BG model consistently predicted radiative cooling power with high accuracy, with mean absolute percentage errors generally below 10 %, outperforming both the effective sky emissivity method and MODTRAN-based predictions. Furthermore, we introduce the concept of band-resolved atmospheric energy databases, analogous to solar radiation databases, and demonstrate it with a full-year case study in Hefei. This work provides a new modeling framework that enhances precision and enables broader applications in energy systems, climate studies, and environmental design.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100244"},"PeriodicalIF":13.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revealing drivers of green technology adoption through explainable Artificial Intelligence 通过可解释的人工智能揭示绿色技术采用的驱动因素

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-18 DOI: 10.1016/j.adapen.2025.100242

Dorothea Kistinger , Maurizio Titz , Philipp C. Böttcher , Michael T. Schaub , Sandra Venghaus , Dirk Witthaut

{"title":"Revealing drivers of green technology adoption through explainable Artificial Intelligence","authors":"Dorothea Kistinger , Maurizio Titz , Philipp C. Böttcher , Michael T. Schaub , Sandra Venghaus , Dirk Witthaut","doi":"10.1016/j.adapen.2025.100242","DOIUrl":"10.1016/j.adapen.2025.100242","url":null,"abstract":"<div><div>Effective governance of energy system transformation away from fossil resources requires a quantitative understanding of the diffusion of green technologies and its key influencing factors. In this article, we propose a novel machine learning approach to diffusion research focusing on actual decisions and spatial aspects complementing research on intentions and temporal dynamics. We develop machine learning models that predict regional differences in the accumulated peak power of household-scale photovoltaic systems and the share of battery electric vehicles from a large set of demographic, geographic, political, and socio-economic features. Tools from explainable artificial intelligence enable a consistent identification of the key influencing factors and quantify their impact. Focusing on data from German municipal associations, we identify common themes and differences in the adoption of green technologies. Specifically, the adoption of battery electric vehicles is strongly associated with income and election results, while the adoption of photovoltaic systems correlates with the prevalence of large dwellings and levels of global solar radiation.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100242"},"PeriodicalIF":13.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances and challenges in energy and climate alignment of AI infrastructure expansion 人工智能基础设施扩展在能源和气候方面的进展和挑战

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-12 DOI: 10.1016/j.adapen.2025.100243

Apoorv Lal , Fengqi You

{"title":"Advances and challenges in energy and climate alignment of AI infrastructure expansion","authors":"Apoorv Lal , Fengqi You","doi":"10.1016/j.adapen.2025.100243","DOIUrl":"10.1016/j.adapen.2025.100243","url":null,"abstract":"<div><div>The rapid growth of artificial intelligence (AI) infrastructure deployment presents significant challenges for global energy systems and climate goals. While previous reviews address the sustainability of traditional data centers, Green AI approaches centered on model-level improvements or the application of AI in advancing sustainability across sectors, the energy and climate consequences of deploying AI infrastructure itself remain underexplored in prior literature. This paper reviews existing analyses on AI infrastructure’s energy and climate implications and proposes quantitative scenario-based frameworks, highlighting key research challenges at the intersection of AI-driven energy demand, region-specific clean energy strategies and their economic competitiveness, strategic levers in energy sourcing decisions, and policy dynamics. Additionally, this work identifies future research directions for aligning AI infrastructure growth with clean energy transitions through targeted mitigation opportunities across spatial and temporal horizons. First, the ambitious investment pathways for AI infrastructure development in the US underscore the need for spatially resolved scenario frameworks that reflect regional differences in deployment patterns and clean energy integration, along with the associated cost trajectories, to guide federal and state regulators. Second, the global expansion of AI infrastructure emphasizes the need for comprehensive frameworks that assess country-specific electricity demand shares, renewable transition pathways, and the influence of geopolitical restrictions, offering actionable insights for climate-conscious strategies. Finally, to prevent reinforcing fossil fuel dependency, particularly under disruptive growth scenarios, energy pathways incorporating nuclear power, renewables, energy storage, and varying grid reliance are explored as part of broader clean energy transitions, especially in regions facing energy security challenges.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100243"},"PeriodicalIF":13.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble learning framework for radiative cooling coatings in China’s buildings 中国建筑辐射冷却涂料的集成学习框架

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-10 DOI: 10.1016/j.adapen.2025.100241

Ze Li , Jianheng Chen , Wenqi Wang , Yang Fu , Xin Li , Aiqiang Pan , Yiying Zhou , Shimelis Admassie , Chi Yan Tso

{"title":"Ensemble learning framework for radiative cooling coatings in China’s buildings","authors":"Ze Li , Jianheng Chen , Wenqi Wang , Yang Fu , Xin Li , Aiqiang Pan , Yiying Zhou , Shimelis Admassie , Chi Yan Tso","doi":"10.1016/j.adapen.2025.100241","DOIUrl":"10.1016/j.adapen.2025.100241","url":null,"abstract":"<div><div>Radiative cooling (RC) coatings have emerged as a promising strategy to mitigate the urban heat island effect and improve energy performance in residential buildings. However, their effect varies significantly across different climate zones and urban configurations, underscoring the need for targeted deployment strategies. In this study, an ensemble learning framework was developed by integrating the urban canopy model with the building energy model to predict the energy performance of RC coatings on residential buildings throughout China. A dataset of 5080 cases was generated, and CatBoost demonstrated excellent predictive accuracy (R<sup>2</sup> = 0.948–0.989). SHapley Additive exPlanations analysis identified longwave radiation and building geometry as the most influential factors affecting RC coating energy performance. The trained prediction model was further applied to evaluate six representative cities across diverse climate zones, for community-level evaluation. Additionally, national-scale predictions were conducted by the framework, using simulations of 111 cities, showing RC coatings are most effective in climate zones with hot summer and warm winter, with maximum annual electricity savings of approximately 50 MWh and maximum carbon emission reductions of around 20 kg·m<sup>-2</sup> per year in a hypothetical residential neighborhood. In contrast, their benefits are more limited in cold climate zones due to increased heating demand. These findings provide an effective framework for optimizing RC coating deployment strategies under varying climatic conditions. Furthermore, the framework holds the potential to expand these analyses globally, enabling the evaluation of RC coatings across diverse building types and regions to support worldwide energy and carbon reduction goals.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100241"},"PeriodicalIF":13.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flexibility potential of electric vehicle charging: A trip chain analysis under bi-criterion stochastic dynamic user equilibrium 电动汽车充电灵活性潜力：双准则随机动态用户均衡下的行程链分析

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-01 DOI: 10.1016/j.adapen.2025.100240

Shuyi Tang, Yunfei Mu, Hongjie Jia, Xiaolong Jin, Xiaodan Yu

{"title":"Flexibility potential of electric vehicle charging: A trip chain analysis under bi-criterion stochastic dynamic user equilibrium","authors":"Shuyi Tang, Yunfei Mu, Hongjie Jia, Xiaolong Jin, Xiaodan Yu","doi":"10.1016/j.adapen.2025.100240","DOIUrl":"10.1016/j.adapen.2025.100240","url":null,"abstract":"<div><div>The widespread adoption of electric vehicles (EVs) creates opportunities to use EV charging load as a flexible resource to improve grid operation. In urban areas, EV users typically follow trip chains in their daily travel, offering temporal and spatial flexibility in EV charging. Specifically, charging at slow-charging spots at trip destinations is temporally flexible when the parking duration exceeds the required charging time. In contrast, charging at fast charging stations (FCSs) during trips is spatially flexible, with route and FCS choice influenced by traffic congestion, FCS charging prices, and user perception. In this paper, we propose a bi-criterion stochastic dynamic user equilibrium (SDUE) model with trip chain demand, which captures route and FCS choice of EV users and derives fast and slow charging loads. The model accounts for user response to traffic congestion and FCS charging prices, along with the randomness in user perception of trip utility. A quantitative evaluation is also presented on the spatial flexibility of fast charging driven by price incentives, and the temporal flexibility of slow charging enabled by long parking durations. A case study in Sioux Falls is conducted to evaluate the flexibility potential of EV charging, revealing that reduced randomness in user perception enhances the spatial flexibility potential of fast charging. Additionally, the temporal flexibility potential of slow charging varies across location types, such as home, work, and other locations, depending on arrival times and parking durations. This research provides key insights for optimizing grid management and enhancing EV integration into power systems.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100240"},"PeriodicalIF":13.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of reinforcement learning in Building-Integrated Photovoltaic (BIPV) optimization 建筑集成光伏（BIPV）优化中强化学习的系统综述

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-01 DOI: 10.1016/j.adapen.2025.100239

Jiaqi Li , Hongbin Xie , Jingyuan Zhang , Lianxin Li , Ge Song , Hongdi Fu , Panxi Chen , Chenyang Liu , Liyu Zhang , Zhuoran Shi , Qing Yu , Xuan Song , Haoran Zhang

{"title":"A systematic review of reinforcement learning in Building-Integrated Photovoltaic (BIPV) optimization","authors":"Jiaqi Li , Hongbin Xie , Jingyuan Zhang , Lianxin Li , Ge Song , Hongdi Fu , Panxi Chen , Chenyang Liu , Liyu Zhang , Zhuoran Shi , Qing Yu , Xuan Song , Haoran Zhang","doi":"10.1016/j.adapen.2025.100239","DOIUrl":"10.1016/j.adapen.2025.100239","url":null,"abstract":"<div><div>Building-Integrated Photovoltaic (BIPV), as an emerging clean energy solution, plays a crucial role in energy saving, emission reduction, and grid load regulation. However, due to the uncertainty of dynamic environments and the complexity of multiple sensitive parameters, traditional scheduling methods fail to achieve optimal results. Considering that reinforcement learning, as an advanced research approach, demonstrates great potential in decision-making for high-dimensional problems and stability in dynamic environments, integrating reinforcement learning with BIPV is a feasible solution to address scheduling challenges in BIPV systems. However, there is still a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in the BIPV field, which, to some extent, limits its further development in the BIPV domain. To this end, this review conducts an in-depth analysis of the effectiveness of reinforcement learning in BIPV applications from the perspective of the system construction life cycle. By considering the algorithm modeling life cycle of reinforcement learning, it comprehensively examines the potential issues in its application to BIPV, highlighting the challenges faced by existing research and future applications. Additionally, this paper integrates cutting-edge reinforcement learning knowledge, summarizes and categorizes its potential applications in BIPV, providing reference guidance for future research directions. Through this systematic review of reinforcement learning applications in the BIPV field, this study aims to offer valuable insights for subsequent research.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100239"},"PeriodicalIF":13.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing flexibility in wind-powered hydrogen production systems through coordinated electrolyzer operation 通过协调电解槽操作，提高风力制氢系统的灵活性

IF 13.8

Advances in Applied Energy Pub Date : 2025-09-01 DOI: 10.1016/j.adapen.2025.100228

Zhang Bai , Wenjie Hao , Qi Li , Rujing Yan , Bin Ding , Weiming Shao , Long Gao , Tieliu Jiang , Yongsheng Wang , Caifeng Wen

{"title":"Enhancing flexibility in wind-powered hydrogen production systems through coordinated electrolyzer operation","authors":"Zhang Bai , Wenjie Hao , Qi Li , Rujing Yan , Bin Ding , Weiming Shao , Long Gao , Tieliu Jiang , Yongsheng Wang , Caifeng Wen","doi":"10.1016/j.adapen.2025.100228","DOIUrl":"10.1016/j.adapen.2025.100228","url":null,"abstract":"<div><div>Wind-powered water electrolysis for hydrogen production is a sustainable and environmentally friendly energy technology. However, the inherent intermittency and variability of wind power, significantly damage the stability and efficiency of the hydrogen production system. To enhance the operational flexibility and system efficiency, a novel wind-hydrogen production system is proposed, which integrates a new coordination of the conventional alkaline electrolyzers (AEL) and proton exchange membrane electrolyzers (PEMEL), for optimizing the dynamic operation of the system under fluctuating wind power. The developed approach employs variational mode decomposition to classify wind power fluctuations into different frequency components, which are then allocated to suitable type of electrolyzers. The configurations of the developed system are optimized using the non-dominated sorting genetic algorithm, and the operating scenarios are dynamically analyzed through clustering techniques. Compared to the AEL-only system, the proposed system demonstrates significant enhancements, with energy efficiency and internal rate of return increased by 5.78 % and 10.65 %, respectively. Meanwhile, the coordinated operation extends the continuous operating time of the AEL by 7.08 %. The proposed approach enhances the economic viability and operational stability of wind-powered hydrogen production, providing a valuable reference for industrial green hydrogen applications.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100228"},"PeriodicalIF":13.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-informed modularized neural network for advanced building control by deep reinforcement learning 基于深度强化学习的先进建筑控制的物理信息模块化神经网络

IF 13.8

Advances in Applied Energy Pub Date : 2025-08-11 DOI: 10.1016/j.adapen.2025.100237

Zixin Jiang, Xuezheng Wang, Bing Dong

{"title":"Physics-informed modularized neural network for advanced building control by deep reinforcement learning","authors":"Zixin Jiang, Xuezheng Wang, Bing Dong","doi":"10.1016/j.adapen.2025.100237","DOIUrl":"10.1016/j.adapen.2025.100237","url":null,"abstract":"<div><div>Physics-informed machine learning (PIML) provides a promising solution for building energy modeling and can be used as a virtual environment to enable reinforcement learning (RL) agents to interact and learn. However, how to integrate physics priors efficiently, evaluate the effectiveness of physics constraints, balance model accuracy and physics consistency, and enable real-world implementation remain open challenges. To address these gaps, this study introduces a Physics-Informed Modularized Neural Network (PI-ModNN), which integrates physics priors through a physics-informed model structure, loss functions, and hard constraints. A new evaluation matrix called “temperature response violation” is developed to quantify the physical consistency of data-driven building dynamic models under varying control inputs and training data sizes. Additionally, a physics prior evaluation framework based on “rule importance” is proposed to quantify the contribution of each individual physical priors, offering guidance on selecting appropriate PIML techniques. The results indicate that incorporating physical priors does not always improve model performance; inappropriate physical priors could decrease model accuracy and consistency. However, hard constraints effectively enforce model consistency. Furthermore, we present a general workflow for developing control-oriented PIML models and integrating them with deep reinforcement learning (DRL). Following this framework, a case study of implementation DRL in an office space for three months demonstrates potential energy savings of 31.4%. Finally, we provide a general guideline for integrating data-driven models with advanced building control through a four-step evaluation framework, paving the way for reliable and scalable implementation of advanced building controls.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100237"},"PeriodicalIF":13.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A critical review of use cases and insights from a large dataset of smart thermostats 对智能恒温器大型数据集的用例和见解进行了批判性回顾

IF 13.8

Advances in Applied Energy Pub Date : 2025-08-08 DOI: 10.1016/j.adapen.2025.100236

Han Li , William O’Brien , Vivian Loftness , Erica Cochran Hameen , Tianzhen Hong

{"title":"A critical review of use cases and insights from a large dataset of smart thermostats","authors":"Han Li , William O’Brien , Vivian Loftness , Erica Cochran Hameen , Tianzhen Hong","doi":"10.1016/j.adapen.2025.100236","DOIUrl":"10.1016/j.adapen.2025.100236","url":null,"abstract":"<div><div>Residential buildings consume a significant portion (17 % in 2023) of the global primary energy. Smart thermostat has become a proven technology in the residential building sector that offers insights into energy efficiency, HVAC system operation, and indoor thermal comfort of occupants. Although there are an increasing number of studies using the available large scale smart thermostat dataset, there lacks a holistic review of the existing literature to understand what applications have been conducted and what outcomes have been offered. This paper reviews 57 articles published between January 2015 and March 2025 using the open access ecobee Donate Your Data (DYD) dataset, where >200,000 customers participated in the voluntary data donation program. Articles are analyzed by major application areas including occupant behavior and IEQ assessment, energy performance evaluation, HVAC operations and controls, and building thermal dynamics. Two major limitations of the DYD dataset are the lack of measured energy use of HVAC systems and the coarse city-level building location information and limits applications requiring energy use data and introduces errors in ignoring the urban microclimate effects influencing a home’s operation and performance. Gaps and challenges of using the ecobee thermostat dataset for research were analyzed. Future efforts should focus on improving data collection and fusing other datasets with the ecobee DYD dataset to unlock new applications and improve analytics accuracy. Furthermore, AI emerges as a powerful tool to help clean up, integrate, and analyze the thermostat dataset, create and calibrate energy models, as well as inferring residential building operation and performance at scale.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"19 ","pages":"Article 100236"},"PeriodicalIF":13.8,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0