Deep Reinforcement Learning and IoT for Renewable Energy Optimization in Smart Buildings: A Comprehensive Review

Abstract

This paper presents the implications of integrating deep reinforcement learning (DRL) and the Internet of Things (IoT) in optimizing energy management, specifically in smart buildings for sustainable urban development. It further explores how DRL, along with real-time IoT sensor-based data, helps improve energy performance in responding to actual HVAC, lighting and renewable energy conditions. Key techniques like genetic algorithms, particle swarm optimization and hybrid techniques are critically examined in maintaining an equilibrium between energy consumption versus renewable sourcing in smart building models. Boundary-preserving strategies and federated learning appear as techniques addressing expansibility and information protection difficulties, notably over IOT systems. Further research would include technology in local processing and situation-responsive DRL to enhance more independent, user-focused and ecologically responsive buildings. This review provides a roadmap for implementing robust, privacy-conscious AI frameworks in smart buildings, underlining their potential to cut energy use and contribute to broader environmental goals.