Semiconductor Optoelectronics, Volume. 41, Issue 5, 717(2020)

Study on Short-to-Medium-Term Photovoltaic Power Generation Forecasting Model Based on Improved Deep Deterministic Policy Gradient

SU Shihui*... LEI Yong, LI Yongkai and ZHU Yingwei |Show fewer author(s)
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    References(5)

    [1] [1] Pierro M, Bucci F, De Felice M, et al. Multi-model ensemble for day ahead prediction of photovoltaic power generation[J]. Solar Energy, 2016, 134(4): 132-146.

    [3] [3] Cheng K, Guo L M, Wang Y K, et al. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network[J]. IOP Conf. Series: Earth and Environmental Science, 2017, 93(6): 120-124.

    [8] [8] Mashud Rana, Ashfaqur Rahman. Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling[J]. Sustainable Energy, Grids and Networks, 2020, 28(3): 21-26.

    [10] [10] Cheng Gang, Song Shaojian, Lin Yuzhang, et al. Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting[J]. Electric Power Systems Research, 2019, 42(10): 177-183.

    [11] [11] Gao Mingming, Li Jianjing, Feng Hong, et al. Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM[J]. Energy, 2019, 18(8): 187-193.

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    SU Shihui, LEI Yong, LI Yongkai, ZHU Yingwei. Study on Short-to-Medium-Term Photovoltaic Power Generation Forecasting Model Based on Improved Deep Deterministic Policy Gradient[J]. Semiconductor Optoelectronics, 2020, 41(5): 717

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    Paper Information

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    Received: Apr. 23, 2020

    Accepted: --

    Published Online: Jan. 19, 2021

    The Author Email: Shihui SU (ma26565061@126.com)

    DOI:10.16818/j.issn1001-5868.2020.05.022

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