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
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML