Forecasting Photovoltaic Power Generation Using Satellite Images

Dukhwan Yu, Seowoo Lee, Sangwon Lee, Wonik Choi and Ling Liu
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Dukhwan Yu: Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea
Seowoo Lee: Power and Industrial Systems R&D Center, Hyosung Corporation, Anyang 14080, Korea
Sangwon Lee: Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea
Wonik Choi: Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea
Ling Liu: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA

Energies, 2020, vol. 13, issue 24, 1-15

Abstract: As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In order to improve the accuracy of PV power generation forecasting, a fair amount of research has been applied to weather forecast data (to a learning process). Despite these efforts, the problems of forecasting PV power generation remains challenging since existing methods show limited accuracy due to inappropriate cloud amount forecast data, which are strongly correlated with PV power generation. To address this problem, we propose a PV power forecasting model, including a cloud amount forecasting network trained with satellite images. In addition, our proposed model adopts convolutional self-attention to effectively capture historical features, and thus acquire helpful information from weather forecasts. To show the efficacy of the proposed cloud amount forecast network, we conduct extensive experiments on PV power generation forecasting with and without the cloud amount forecast network. The experimental results show that the Mean Absolute Percentage Error (MAPE) of our proposed prediction model, combined with the cloud amount forecast network, are reduced by 22.5% compared to the model without the cloud amount forecast network.

Keywords: microgrid; virtual power plant (VPP); PV power generation forecasting; cloud amount forecasting; deep learning; convolutional self-attention; LSTM (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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