Implementing Extreme Gradient Boosting (XGBoost) Algorithm in Predicting Solar Irradiance
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Abstract
Globally, the consistent clamor by environmentalists for the need to mitigate the effects of climate change has necessitated the adoption of renewable energy sources (RES) for use by many developed/developing nations. The approach is geared towards gradually replacing or reducing the use of fossil fuels for electric power production. Solar power is among the major renewable energy sources in use today. But the use of solar energy is, unfortunately, characterized by fluctuations in its power generation due to the unpredictability of solar irradiance. Despite many methods in use already, accurate forecasting of solar irradiance has continued to be a great need both in the field of physical simulations and artificial intelligence. In this paper, an extreme gradient boosting (XGBoost) regression algorithm was deployed to successfully predict solar power with minimal error. Eighty percent of one-year and five years of historical hourly solar irradiance data of Johannesburg city were separately used as the training dataset. The results obtained using this algorithm were compared with the ones from Support Vector Machine (SVM), to determine the model with the least forecast errors. The results showed that the XGBoost model with an nRMSE value of 6.63% performed better than the SVM model with 6.81%. It is hoped that the implementation of the XGB algorithm for solar irradiance forecasts could greatly improve the stability of solar electric power generated for optimum connection to the power grid.