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Predicting Energy Production in Renewable Energy Power Plants Using Deep Learning

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  • Abdil Karakan

    (Electrical Department, Dazkırı Vocational School, Afyon Kocatepe University, Afyonkarahisar 03204, Turkey)

Abstract

It is very important to analyze and forecast energy production for investments in renewable energy resources. In this study, the energy production of wind and solar power plants, which are among the leading renewable energy sources, was estimated using deep learning. For a solar power plant, three different solar power plants with 1MW installed power were examined. Three-year energy production data of power plants were taken. These data were used with the deep learning method long short-term memory (LSTM) and seasonal autoregressive moving average (SARIMA). Results were obtained for each dataset; they were subjected to five different (MSE, RMSE, NMSE, MAE, and MAPE) error performance measurement systems. In the LSTM model, the highest accuracy rate was 81% and the lowest accuracy rate was 59%. In the SARIMA model, the highest accuracy rate was 66% and the lowest accuracy rate was 41%. As for wind energy, wind speeds in two different places were estimated. Wind speed data were taken from meteorological stations. Datasets were tested with MAPE, R 2 , and RMSE error performance measurement systems. LSTM, GRU, CNN-LSTM, CNN-RNN, LSTM-GRU, and CNN-GRU deep learning methods were used in this study. The CNN-GRU model achieved a maximum accuracy of 99.81% in wind energy forecasting.

Suggested Citation

  • Abdil Karakan, 2024. "Predicting Energy Production in Renewable Energy Power Plants Using Deep Learning," Energies, MDPI, vol. 17(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4031-:d:1456154
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    References listed on IDEAS

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    3. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
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