  Energy management systems (EMS) have classically been implemented based on
rule-based control (RBC) and model predictive control (MPC) methods. Recent
research are investigating reinforcement learning (RL) as a new promising
approach. This paper introduces TreeC, a machine learning method that uses the
metaheuristic algorithm covariance matrix adaptation evolution strategy
(CMA-ES) to generate an interpretable EMS modeled as a decision tree. This
method learns the decision strategy of the EMS based on historical data
contrary to RBC and MPC approaches that are typically considered as non
adaptive solutions. The decision strategy of the EMS is modeled as a decision
tree and is thus interpretable contrary to RL which mainly uses black-box
models (e.g. neural networks). The TreeC method is compared to RBC, MPC and RL
strategies in two study cases taken from literature: (1) an electric grid case
and (2) a household heating case. The results show that TreeC obtains close
performances than MPC with perfect forecast in both cases and obtains similar
performances to RL in the electrical grid case and outperforms RL in the
household heating case. TreeC demonstrates a performant application of machine
learning for energy management systems that is also fully interpretable.
