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 longterm 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.

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