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.
