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- W4214663236 abstract "Residential demand response is recognized as an affordable and promising method to maintain the stable operation of the smart grid. Fueled by the large-scale employment of smart meters, retailers can collect and understand consumers' energy consumption behaviors with convenience. This paper focuses on proposing deep learning aided customized pricing strategy for residential consumers to facilitate the demand response. First, a temporal pattern attention-based long short-term memory network is applied to learn consumer's energy consumption behavior affected by price. Then, based on this behavior learning model, a customized pricing strategy is suggested to the retailer to design a dynamic pricing mechanism for the consumer for engaging in the demand response program. Numerical studies demonstrate that the applied energy consumption behavior learning model can extract complicated characteristics and feature relevance to learn consumer behavior with high accuracy, and the proposed customized pricing strategy can greatly promote the demand response program while the price risk is also hedged to a great extent." @default.
- W4214663236 created "2022-03-02" @default.
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- W4214663236 date "2021-10-22" @default.
- W4214663236 modified "2023-10-16" @default.
- W4214663236 title "Customized Pricing Strategy of Residential Consumers in Retailers for Demand Response" @default.
- W4214663236 doi "https://doi.org/10.1109/ei252483.2021.9713292" @default.
- W4214663236 hasPublicationYear "2021" @default.
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