Deep Reinforcement Learning for Building Control : A comparative study for applying Deep Reinforcement Learning to Building Energy Management

Sammanfattning: Energy and environment have become hot topics in the world. The building sector accounts for a high proportion of energy consumption, with over one-third of energy use globally. A variety of optimization methods have been proposed for building energy management, which are mainly divided into two types: model-based and model-free. Model Predictive Control is a model-based method but is not widely adopted by the building industry as it requires too much expertise and time to develop a model. Model-free Deep Reinforcement Learning(DRL) has successful applications in game-playing and robotics control. Therefore, we explored the effectiveness of the DRL algorithms applied to building control and investigated which DRL algorithm performs best. Three DRL algorithms were implemented, namely, Deep Deterministic Policy Gradient(DDPG), Double Deep Q learning(DDQN) and Soft Actor Critic(SAC). We used the building optimization testing (BOPTEST) framework, a standardized virtual testbed, to test the DRL algorithms. The performance is evaluated by two Key Performance Indicators(KPIs): thermal discomfort and operational cost. The results show that the DDPG agent performs best, and outperforms the baseline with the saving of thermal discomfort by 91.5% and 18.3%, and the saving of the operational cost by 11.0% and 14.6% during the peak and typical heating periods, respectively. DDQN and SAC agents do not show a clear advantage of performance over the baseline. This research highlights the excellent control performance of the DDPG agent, suggesting that the application of DRL in building control can achieve a better performance than the conventional control method.