Machine Learning Applications in Building Energy Systems: Review and Prospects

Buildings, Journal Year: 2025, Volume and Issue: 15(4), P. 648 - 648

Published: Feb. 19, 2025

Building energy systems (BESs) are essential for modern infrastructure but face significant challenges in equipment diagnosis, consumption prediction, and operational control. The complexity of BESs, coupled with the increasing integration renewable sources, presents difficulties fault detection, accurate forecasting, dynamic system optimisation. Traditional control strategies struggle low efficiency, slow response times, limited adaptability, making it difficult to ensure reliable operation optimal management. To address these issues, researchers have increasingly turned machine learning (ML) techniques, which offer promising solutions improving scheduling, real-time BESs. This review provides a comprehensive analysis ML techniques applied According results literature review, supervised methods, such as support vector machines random forest, demonstrate high classification accuracy detection require extensive labelled datasets. Unsupervised approaches, including principal component clustering algorithms, robust identification capabilities without data may complex nonlinear patterns. Deep particularly convolutional neural networks long short-term memory models, exhibit superior forecasting Reinforcement further enhances management by dynamically adjusting parameters maximise efficiency cost savings. Despite advancements, remain terms availability, computational costs, model interpretability. Future research should focus on hybrid integrating explainable AI enhancing adaptability evolving demands. also highlights transformative potential BESs outlines future directions sustainable intelligent building

Language: Английский

---

Inclusive green finance approach to assess energy resilience: Integrating artificial intelligence (AI) utilization in energy strategy perspective

Energy Strategy Reviews, Journal Year: 2025, Volume and Issue: 59, P. 101696 - 101696

Published: April 7, 2025

Language: Английский

---