Simulation based predictive control of low-energy building systems using two-stage optimization

Simulation based control schemes for a low-energy building system are introduced and compared in this paper. The simulation of a low-energy system is firstly constructed and a fast two-stage optimisation method is proposed to find the optimal control policy in short time. A Model Predictive Control (MPC) scheme and a Hierarchical Fuzzy Rule based Control (HFRC) scheme that is tuned online by a reinforcement learning (RL) agent are introduced. The MPC scheme runs the simulation online to predict the future behaviour in order to make long-term optimal decisions. On the other hand, the HFRC+RL scheme run the simulation offline to generate prior knowledge for the RL agent. The performances of the different schemes are evaluated by comparing energy consumption, thermal comfort and computing time.