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Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Author

Listed:
  • Hyung Keun Ahn

    (The Department of Electrical & Electronics Engineering, Konkuk University, Seoul 05029, Korea)

  • Neungsoo Park

    (The Department of Computer Science & Engineering, Konkuk University, Seoul 05029, Korea)

Abstract

Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R 2 -scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R 2 -scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.

Suggested Citation

  • Hyung Keun Ahn & Neungsoo Park, 2021. "Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors," Energies, MDPI, vol. 14(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:436-:d:480658
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    References listed on IDEAS

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    Cited by:

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    6. Cai Tao & Junjie Lu & Jianxun Lang & Xiaosheng Peng & Kai Cheng & Shanxu Duan, 2021. "Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network," Energies, MDPI, vol. 14(11), pages 1-16, May.
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