A Hybrid Approach for Home Energy Management With Imitation Learning and Online Optimization
Shuhua Gao, R. Lee, Zhen-hao Huang, Cheng Xiang, Ming Yu, Kuan Tak Tan, Tong Heng Lee
Abstract
A home energy management system exploits the time-varying electricity tariff and renewable energy profiles to lower residents' electricity bills via wise scheduling of various domestic appliances. This study targets the rather typical case of a general household with solar panels. All four classes of loads are considered, while many existing studies only investigate a restricted subset. Considering the high stochasticity in real-time pricing and solar power generation, we propose an online approach in a hybrid semidecentralized framework, where each shiftable load is controlled by a deep neural network (DNN), and all adjustable loads are coordinated together by fast online optimization. We train each DNN via efficient and effective imitation learning (IL) instead of popular reinforcement learning (RL). This framework allows adjustable loads to react properly to possibly poor actions of shiftable loads via online one-step optimization to alleviate their adverse impact. Numerical experiments with real-world data show that, compared with RL, our approach can reduce the training time significantly, while its execution time is only slightly affected. Moreover, our approach outperforms the traditional day-ahead optimization method and the fully decentralized multiagent RL and multiagent IL methods by a wide margin, attaining an average cost fairly close to the theoretical minimum.