AI-Guided Energy Optimization In Hvac System

Abstract

This paper proposes a predictive analysis framework for Heating, Ventilation, and Air Conditioning (HVAC) systems using Reinforcement Learning (RL) techniques. The framework aims to optimize the energy consumption of HVAC systems by predicting the optimal set- points for HVAC controllers. RL algorithms, specifically Q- Learning and Deep Q-Network (DQN), are applied to learn the optimal set-points based on historical data and real-time feedback. The approach is evaluated using real-world data from an HVAC system in a commercial building. The results show that the RL-based approach can achieve significant energy savings while maintaining a comfortable indoor temperature. This study demonstrates the potential of RL techniques for predictive analysis and optimization of HVAC systems using model free deep RL techniques and Influx db as database while analyzing the performance better using Grafana visualization tool.