Electrical engineering

Energy (signal processing)

Scheduling (production processes)

Demand response

Mathematics

Energy management

Reinforcement learning

Statistics

Markov decision process

Mathematical optimization

Engineering

Artificial intelligence

Markov process

Q-learning

Electricity

Operations management

Energy consumption

Computer science

Photovoltaic system

Real-time computing

IEEE Transactions on Smart Grid

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.

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