TreeC: A method to generate interpretable energy management systems using a metaheuristic algorithm

Abstract

Energy management systems (EMS) have traditionally been implemented using rule-based control (RBC) and model predictive control (MPC) methods. However, recent research has explored the use of reinforcement learning (RL) as a promising alternative. This paper introduces TreeC, a machine learning method that utilises the covariance matrix adaptation evolution strategy metaheuristic algorithm to generate an interpretable EMS modelled as a decision tree. Unlike RBC and MPC approaches, TreeC learns the decision strategy of the EMS based on historical data, adapting the control model to the controlled energy grid. The decision strategy is represented as a decision tree, providing interpretability compared to RL methods that often rely on black-box models like neural networks. TreeC is evaluated against MPC with perfect forecast and RL EMSs in two case studies taken from literature: an electric grid case and a household heating case. In the electric grid case, TreeC achieves an average energy loss and constraint violation score of 19.2, which is close to MPC and RL EMSs that achieve scores of 14.4 and 16.2 respectively. All three methods control the electric grid well especially when compared to the random EMS, which obtains an average score of 12 875. In the household heating case, TreeC performs similarly to MPC on the adjusted and averaged electricity cost and total discomfort (0.033 EUR/m² and 0.42 Kh for TreeC compared to 0.037 EUR/m² and 2.91 kH for MPC), while outperforming RL (0.266 EUR/m² and 24.41 Kh). TreeC demonstrates a performant and interpretable application of machine learning for EMSs.