Optimizing Comfort and Energy Efficiency: Smart HVAC Control with Reinforcement Learning and Time Series Forecasting

Abstract

The Deep Reinforcement Learning (DRL) Framework offers a sophisticated solution for optimizing HVAC control strategies in diverse building environments, utilizing deep learning algorithms and reinforcement learning. Adaptable to various building types and HVAC systems, the framework enhances energy efficiency and occupant comfort. Successful implementation requires consideration of system complexity, data availability, and computational resources. Integrating time series forecasting models, specifically 1D CNN and LSTM, with reinforcement learning proves effective in predicting system parameters. These predictions guide the reinforcement learning agent in making sequential decisions for HVAC control actions, resulting in improved total rewards and validation loss during training. This holistic approach, supported by experiments, demonstrates tangible benefits in achieving optimal HVAC system management, highlighting increased energy efficiency and cost-effectiveness through the synergy of predictive analytics and adaptive control.