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

Hyung Keun Ahn and Neungsoo Park
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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

Energies, 2021, vol. 14, issue 2, 1-17

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.

Keywords: Internet of Things (IoT); photovoltaic power forecasting algorithm; recurrent neural networks (RNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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