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

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 ti...

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Veröffentlicht in:IEEE transactions on smart grid Jg. 11; H. 4; S. 3201 - 3211
Hauptverfasser: Xu, Xu, Jia, Youwei, Xu, Yan, Xu, Zhao, Chai, Songjian, Lai, Chun Sing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q -learning algorithm
Algorithms
Artificial neural networks
Computer simulation
data-driven method
Decision making
demand response
Electric appliances
Electric vehicles
Electricity pricing
Energy consumption
Energy management
finite Markov decision process
Home appliances
home energy management
Household appliances
Machine learning
Markov analysis
Markov processes
Multiagent systems
neural network
Neural networks
Optimization
Photovoltaic cells
Reinforcement learning
Residential energy
Schedules
Scheduling
Uncertainty
Windows (intervals)
ISSN:1949-3053, 1949-3061
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Zusammenfassung: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 <inline-formula> <tex-math notation="LaTeX">{Q} </tex-math></inline-formula>-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.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2020.2971427