Journal
ENERGIES
Volume 13, Issue 24, Pages -Publisher
MDPI
DOI: 10.3390/en13246603
Keywords
microgrid; virtual power plant (VPP); PV power generation forecasting; cloud amount forecasting; deep learning; convolutional self-attention; LSTM
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Funding
- Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea [20194310100030]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20194310100030] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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