The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network
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 non-shiftable, 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.
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
A Comprehensive Survey of Multiagent Reinforcement Learning
Busoniu, L., Babuska, R., & De Schutter, B. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.913919 · Cited by 2,235
Electricity price forecasting: A review of the state-of-the-art with a look into the future
Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
Jaakkola, T., Jordan, M. I., & Singh, S. P. (1994) · Neural Computation · doi:10.1162/neco.1994.6.6.1185 · Cited by 770
Recent advances in electricity price forecasting: A review of probabilistic forecasting
Nowotarski, J., & Weron, R. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.234 · Cited by 552
A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach
Lu, R., Hong, S. H., & Zhang, X. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.072 · Cited by 430
Self-citationPaterakis, N. G., Erdinc, O., Bakirtzis, A. G., & Catalao, J. P. S. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2438534 · Cited by 429
Residential Demand Response: Dynamic Energy Management and Time-Varying Electricity Pricing
Muratori, M., & Rizzoni, G. (2016) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2015.2414880 · Cited by 397
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Lu, R., & Hong, S. H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.12.061 · Cited by 394
Self-citation
Cited by
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
Cao, D., Hu, W., Zhao, J., Zhang, G., Zhang, B., Liu, Z., Chen, Z., & Blaabjerg, F. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000552 · Cited by 460
A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management
Xu, X., Jia, Y., Xu, Y., Xu, Z., Chai, S., & Lai, C. S. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.2971427 · Cited by 420
Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids
Chung, H.-M., Maharjan, S., Zhang, Y., & Eliassen, F. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3007167 · Cited by 120
Metadata
- Authors
- Lu, R., Hong, S. H., & Yu, M.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2019-11
- Volume
- 10
- Issue
- 6
- Pages
- 6629-6639
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2926295867
- Cited by
- 389
- References
- 32
- FWCI
- 16.30
- Type
- journal article
- Language
- en
- Open access
- closed
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