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    ARTICLE

    Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

    Yunlei Zhang1, Ruifeng Cao1, Danhuang Dong2, Sha Peng3,*, Ruoyun Du3, Xiaomin Xu3

    Energy Engineering, Vol.119, No.5, pp. 1829-1841, 2022, DOI:10.32604/ee.2022.020118

    Abstract In the electricity market, fluctuations in real-time prices are unstable, and changes in short-term load are determined by many factors. By studying the timing of charging and discharging, as well as the economic benefits of energy storage in the process of participating in the power market, this paper takes energy storage scheduling as merely one factor affecting short-term power load, which affects short-term load time series along with time-of-use price, holidays, and temperature. A deep learning network is used to predict the short-term load, a convolutional neural network (CNN) is used to extract the features, and a long short-term memory… More >

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