A Comprehensive Survey of Intelligent Control Approaches in Building Energy Management

Effective building energy management is crucial for mitigating climate change and reducing energy costs. This review focuses on intelligent control methods for optimizing building energy consumption, drawing on recent research from international publications. It examines various strategies, including fuzzy logic control, neural network-predictive control, and reinforcement learning, highlighting their features, advantages, and limitations. The review addresses key challenges, such as controlling complex and unpredictable building systems. Future research directions are explored, emphasizing the development of adaptive, robust, and nonlinear control techniques. This includes integrating machine learning and data-driven approaches to enhance performance, adaptability, and resilience.