
Developments in the Built Environment, Год журнала: 2024, Номер unknown, С. 100578 - 100578
Опубликована: Ноя. 1, 2024
Язык: Английский
Developments in the Built Environment, Год журнала: 2024, Номер unknown, С. 100578 - 100578
Опубликована: Ноя. 1, 2024
Язык: Английский
Buildings, Год журнала: 2025, Номер 15(7), С. 994 - 994
Опубликована: Март 21, 2025
With the rapid advancement of machine learning (ML) technologies, their innovative applications in enhancing building energy efficiency are increasingly prominent. Utilizing tools such as VOSviewer and Bibliometrix, this study systematically reviews body related literature, focusing on key emerging trends cutting-edge ML techniques, including deep learning, reinforcement unsupervised optimizing performance managing carbon emissions. First, paper delves into role prediction, intelligent management, sustainable design, with particular emphasis how smart systems leverage real-time data analysis prediction to optimize usage significantly reduce emissions dynamically. Second, summarizes technological evolution future sector identifies critical challenges faced by field. The findings provide a technology-driven perspective for advancing sustainability construction industry offer valuable insights research directions.
Язык: Английский
Процитировано
1Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115440 - 115440
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115607 - 115607
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Energy and Buildings, Год журнала: 2025, Номер 336, С. 115546 - 115546
Опубликована: Март 5, 2025
Язык: Английский
Процитировано
0Building and Environment, Год журнала: 2025, Номер unknown, С. 112902 - 112902
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115614 - 115614
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Highlights in Science Engineering and Technology, Год журнала: 2025, Номер 137, С. 58 - 63
Опубликована: Апрель 10, 2025
Building Energy Consumption Modeling and Prediction (BECMP) gradually become more significant in architectural engineering construction process dealing with energy efficiency, sustainability environmental-friendly development goals. Based on recent research BECMP, this paper mainly focuses the hybrid model combining physic-based AI-driven model, to discuss improvement simulation result accuracy, prediction residence activity, elimination performance gaps, optimization Artificial Intelligence (AI) algorithm selection improving building rather than other two types of models, following specific application examples evidence. It also discusses opposing concepts models BECMP among scholars limitations prediction, including corporation behavior low popularity topic high education requirements users. Lastly, highlights importance real-time data system monitor management, suggests multidisciplinary collaboration, appeals widespread attention BECMP.
Язык: Английский
Процитировано
0Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112579 - 112579
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Building and Environment, Год журнала: 2025, Номер unknown, С. 113124 - 113124
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 111643 - 111643
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
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