The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Residential Demand Response Using Reinforcement Learning
Abstract
We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from 16% to 40% with respect to a price-unaware energy allocation.
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Cited by
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Fang, X., Misra, S., Xue, G., & Yang, D. (2012) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2011.101911.00087 · Cited by 3,405
Reinforcement learning for demand response: A review of algorithms and modeling techniques
Vázquez-Canteli, J. R., & Nagy, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.11.002 · Cited by 883
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2834219 · Cited by 649
Review on the research and practice of deep learning and reinforcement learning in smart grids
China Electric Power Research Institute, Zhang, D., Han, X., Taiyuan University of Technology, Deng, C., & China Electric Power Research Institute (2018) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2018.00520 · Cited by 561
Metadata
- Authors
- O'Neill, D., Levorato, M., Goldsmith, A., & Mitra, U.
- Venue
- 2010 First IEEE International Conference on Smart Grid Communications
- Publisher
- IEEE
- Published
- 2010-10
- Pages
- 409-414
- OpenAlex
- W2012160694
- Cited by
- 281
- References
- 1
- FWCI
- 26.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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- O'Neill, D., Levorato, M., Goldsmith, A., & Mitra, U. (2010). Residential Demand Response Using Reinforcement Learning. 2010 First IEEE International Conference on Smart Grid Communications. https://doi.org/10.1109/smartgrid.2010.5622078
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
