ew 18: e20

Research Article

Applying Artificial Intelligence in Forecasting the Output of Industrial Solar Power Plant in Vietnam

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  • @ARTICLE{10.4108/eai.29-3-2021.169166,
        author={Ninh Nguyen Quang and Linh Bui Duy and Binh Doan Van and Quang Nguyen Dinh},
        title={Applying Artificial Intelligence in Forecasting the Output of Industrial Solar Power Plant in Vietnam},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2021},
        month={3},
        keywords={Long Short -- Term Memory, Industrial PV power plant, Forecasting PV power, Artificial Intelligence},
        doi={10.4108/eai.29-3-2021.169166}
    }
    
  • Ninh Nguyen Quang
    Linh Bui Duy
    Binh Doan Van
    Quang Nguyen Dinh
    Year: 2021
    Applying Artificial Intelligence in Forecasting the Output of Industrial Solar Power Plant in Vietnam
    EW
    EAI
    DOI: 10.4108/eai.29-3-2021.169166
Ninh Nguyen Quang1,*, Linh Bui Duy1, Binh Doan Van1, Quang Nguyen Dinh1
  • 1: Institute of Energy Science, Vietnam Academy of Science and Technology, Hanoi, Vietnam
*Contact email: nqninh@ies.vast.vn

Abstract

This paper uses recurrent neural network (Long Short – Term Memory - LSTM network) to build a model to forecast short-term generation capacity of Phong Dien solar power plant, (48 MWp – 35 MWAC) located in Thua Thien Hue Province, Viet Nam, with input factors including meteorological parameters. The authors conducted experiments to find the optimal structure of the model corresponding to the conditions of the plant and the data collection. Through this model, meteorological forecast data sets from commercial suppliers were used to forecast the plant's output power. The comments about the result as well as the further study direction are analysed and suggested.