Simulation-based Model Learning for Optimization of Building Energy Management.

In this paper, we present a reinforcement learning based method for building energy management, which introduces a transition-model learning from building energy simulation software. The transition-model receives state from simulation model and returns cost-to-go to control strategy. The method is intended to combat two main gaps when integrating simulation program and optimization algorithm in building energy management, which are nonanalytical and nonlinear computation for simulation program and requirement of optimization algorithms for system dynamics. The method is realized on EnergyPlus, which interact with python program in functional mock-up interface (FMI) standard. The numerical test results show that the transition-model introduced can estimate cost-to-go to a certain extent and significantly improve efficiency of control.