A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management

By Xu Xu, Youwei Jia, Yan Xu, Zhao Xu, Songjian Chai, Chun Sing Lai. IEEE Transactions on Smart Grid, 2020

354 citations recorded by the source corpus.

This paper proposes a novel framework for home energy management (HEM) based on reinforcement learning in achieving efficient home-based demand response (DR). The concerned hour-ahead energy consumption scheduling problem is duly formulated as a finite Markov decision process (FMDP) with discrete time steps. To tackle this problem, a data-driven method based on neural network (NN) and ${Q}$ -learning algorithm is developed, which achieves superior performance on cost-effective schedules for HEM system. Specifically, real data of electricity price and solar photovoltaic (PV) generation are timely processed for uncertainty prediction by extreme learning machine (ELM) in the rolling time windows. The scheduling decisions of the household appliances and electric vehicles (EVs) can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV and several PV panels. The test results demonstrate the effectiveness of the proposed data-driven based HEM framework.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8d06a30a270ff6dc763458dcedeea500fbf249e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Frede Blaabjerg
2. Zhe Chen
3. Yujie Tang
4. Xin Chen
5. Guannan Qu
6. Steven H. Low
7. Na Li
8. Xiongfei Wang

In Electrical and Electronic Engineering

1. Na Li
2. Steven H. Low
3. Yujie Tang
4. Zhe Chen
5. Frede Blaabjerg
6. Xin Chen
7. Guannan Qu
8. Xiongfei Wang

Citations in the corpus

• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges (2021, background): “Some Q -learning-based work [133] discretizes the continuous action space to fit the algorithm framework.”
• Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review (2020, methodology): “Reference [105] [106] [107] [108] [109] [110] [111] [112] [113] Field Demand-side management Distribution network Demand-side management Operational control Operational control Operational control Distribution network Demand response Distribution network Algorithm DQN DQN Q-learning DQN MADDPG MADDP”