A Deep Q-Network for Balancing Energy Savings and Occupant Comfort in an Air Conditioned Environment
TL;DR
A Smart Air Conditioning Management System based on a Deep Q-Network agent capable of dynamically balancing energy use and thermal comfort and demonstrates that reinforcement learning enables adaptive AC control, offering a scalable approach to energy-efficient building management.
Abstract
Rising electricity demand and high energy costs, particularly in rapidly growing regions, highlight the need for more efficient building energy management. Air conditioning (AC) systems account for a substantial portion of electricity consumption, yet conventional fixed-temperature control lacks adaptability to varying environmental conditions. This study introduces a Smart Air Conditioning Management System based on a Deep Q-Network (DQN) agent capable of dynamically balancing energy use and thermal comfort. The system integrates IoT-enabled environmental sensing and actuation, using ESP32 and Raspberry Pi microcontrollers, along with user feedback for automated control of AC units. Initial training and evaluation were conducted in recreated classroom-laboratory environments using simulations in EnergyPlus, and experimental deployment validated performance in real rooms. Simulation results show up to 81% energy reduction under low comfort prioritization, while field deployment achieved approximately 60% savings. These findings demonstrate that reinforcement learning enables adaptive AC control, offering a scalable approach to energy-efficient building management.