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Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

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  • Azizi, Narjes
  • Yaghoubirad, Maryam
  • Farajollahi, Meisam
  • Ahmadi, Abolfzl

Abstract

Solar radiation's intermittent and fluctuating nature poses severe limitations for most applications. Accurate prediction of solar radiation is an essential factor in predicting the output power of a photovoltaic power system. For this purpose, the potential of the 20 MW solar photovoltaic power plant in Zahedan city has been evaluated in this article. With the help of monthly data (1984–2021) and MLP, LSTM, GRU, CNN, and CNN-LSTM models, solar radiation and temperature are predicted for the next ten years. CNN exhibits the best performance compared to other models with four input parameters: global horizontal irradiance, temperature, surface pressure, relative humidity (RH), and two outputs of temperature and radiation. The root mean square error values for global horizontal irradiance and temperature were 12.68 W/m2 and 1.75 °C, respectively. Relative humidity exhibited more significant effect on the model in comparison with surface pressure. Finally, the average annual power output for ten years from 2022 to 2031 is calculated and predicted to be 50.37 GWh.

Suggested Citation

  • Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.
    • Handle: RePEc:eee:renene:v:206:y:2023:i:c:p:135-147
    DOI: 10.1016/j.renene.2023.01.102
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