AI-Based Optimization Of HVAC Systems For Smart EnergyEfficient Buildings

Abstract

This work describes an artificial intelligence (AI)-based optimization strategy for increasing the energy performance of HVAC systems in smart buildings. We employ machine learning algorithms, specifically artificial neural networks (ANN) and reinforcement learning (RL), to analyze energy consumption patterns and optimize control strategies for preserving interior thermal comfort while conserving energy. Simulations were run with EnergyPlus and real-world data from smart buildings. The proposed AI-based technique lowered energy consumption by up to 25% compared to traditional rule-based controls. This study describes a scalable system for intelligent energy management in next-generation buildings.