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Short-term forecasting of global solar irradiance in tropical environments with incomplete data

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  • Hoyos-Gómez, Laura S.
  • Ruiz-Muñoz, Jose F.
  • Ruiz-Mendoza, Belizza J.

Abstract

Electricity access is a common issue around the world. Many countries that face this problem are located in the tropical region and solar energy might be an alternative to mitigate this limitation. Accurate mechanisms for forecasting solar irradiance and insolation provide important information regarding the potential for generating solar energy. Furthermore, this data is relevant for the planning of renewable energy projects, and energy policy formulation. This research introduces a pipeline for the one-day ahead forecasting of solar irradiance and insolation that only requires solar irradiance historical data for training. Our approach includes a data imputation stage to handle missing data. In the prediction stage, we consider four data-driven approaches: Autoregressive Integrated Moving Average, Single Layer Feed Forward Network, Multiple Layer Feed Forward Network, and Long Short-Term Memory. The experiments are performed in a real-world dataset collected by 12 Automatic Weather Stations located in Nariño - Colombia. Our results show that the neural network-based models outperform Autoregressive Integrated Moving Average in most cases, and that Long Short-Term Memory exhibits better performance in cloudy environments (where more randomness is expected).

Suggested Citation

  • Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921014616
    DOI: 10.1016/j.apenergy.2021.118192
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