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.

翻译：能源管理系统（EMS）传统上基于规则控制（RBC）和模型预测控制（MPC）方法实现。近期研究将强化学习（RL）视为一种有前景的新方法。本文提出TreeC方法，这是一种利用元启发式算法——协方差矩阵适应进化策略（CMA-ES）——生成基于决策树的可解释性EMS的机器学习方法。与通常被视为非自适应解决方案的RBC和MPC方法不同，该方法基于历史数据学习EMS的决策策略。同时，与主要使用黑箱模型（如神经网络）的RL方法不同，EMS的决策策略采用决策树建模，因而具有可解释性。在两个文献中的案例研究中，将TreeC方法与RBC、MPC和RL策略进行对比：（1）电网案例，（2）家庭供暖案例。结果表明，在两种案例中，TreeC获得的性能均接近具有完美预测的MPC；在电网案例中与RL性能相当，在家庭供暖案例中则优于RL。TreeC展示了机器学习在能源管理系统中兼具高性能与完全可解释性的有效应用。