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Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

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  • Donghun Lee

    (Department of Industrial and Management Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 407-772, Korea)

  • Kwanho Kim

    (Department of Industrial and Management Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 407-772, Korea)

Abstract

Recently, the prediction of photovoltaic (PV) power has become of paramount importance to improve the expected revenue of PV operators and the effective operations of PV facility systems. Additionally, the precise PV power output prediction in an hourly manner enables more sophisticated strategies for PV operators and markets as the electricity price in a renewable energy market is continuously changing. However, the hourly prediction of PV power outputs is considered as a challenging problem due to the dynamic natures of meteorological information not only in a day but also across days. Therefore, in this paper, we suggest three PV power output prediction methods such as artificial neural network (ANN)-, deep neural network (DNN)-, and long and short term memory (LSTM)-based models that are capable to understand the hidden relationships between meteorological information and actual PV power outputs. In particular, the proposed LSTM based model is designed to capture both hourly patterns in a day and seasonal patterns across days. We conducted the experiments by using a real-world dataset. The experimental results show that the proposed ANN based model fails to yield satisfactory results, and the proposed LSTM based model successfully better performs more than 50% compared to the conventional statistical models in terms of mean absolute error.

Suggested Citation

  • Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:215-:d:196680
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    References listed on IDEAS

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