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

• Published in: Applied energy 2022-02, Vol.307, p.118192, Article 118192
• Main Authors: Hoyos-Gómez, Laura S., Ruiz-Muñoz, Jose F., Ruiz-Mendoza, Belizza J.
• Format: Article
• Language: English
• Subjects: ARIMA; Artificial neural network; Clear sky index; Imputation; Long Short-Term Memory; Solar forecasting
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.118192

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). •Forecasting the clear sky index facilitates the use of solar energy.•Forecasting solar energy could promote, agriculture, and ecology studies.•This research provides guidance for the development of forecasting models.•This research provides guidance for the fine-tuning of forecasting models.•We proposed a framework that uses linear and non-linear data-driven models.•The accuracy depends on the environmental conditions and amount of training data.•This framework can be used for solar irradiance forecasting at any latitude.