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Demand Response for Home Energy Management using Reinforcement Learning and Artificial Neural Network

Title
Demand Response for Home Energy Management using Reinforcement Learning and Artificial Neural Network
Author
홍승호
Keywords
Artificial intelligence; reinforcement learning; artificial neural network; demand response; home energy management
Issue Date
2019-11
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON SMART GRID, v. 10, No. 6, Page. 6629-6639
Abstract
Ever-changing variables in the electricity market require energy management systems (EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest approach being used to accelerate the efficiency and stability of power systems. This paper proposes an hour-ahead DR algorithm for home EMSs. To deal with the uncertainty in future prices, a steady price prediction model based on artificial neural network is presented. In cooperation with forecasted future prices, multi-agent reinforcement learning is adopted to make optimal decisions for different home appliances in a decentralized manner. To verify the performance of the proposed energy management scheme, simulations are conducted with nonshiftable, shiftable, and controllable loads. Experimental results demonstrate that the proposed DR algorithm can handle energy management for multiple appliances, minimize user energy bills, and dissatisfaction costs, and help the user to significantly reduce its electricity cost compared with a benchmark without DR.
URI
https://ieeexplore.ieee.org/document/8681422https://repository.hanyang.ac.kr/handle/20.500.11754/122175
ISSN
1949-3053; 1949-3061
DOI
10.1109/TSG.2019.2909266
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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