Application of explainable machine learning for estimating direct and diffuse components of solar irradiance.

Abstract

The inclusion of diffuse horizontal irradiance (DHI) and direct normal irradiance (DNI) is crucial in the context of solar energy applications. However, most solar irradiance instruments primarily prioritize the measurement of global horizontal irradiance (GHI) due to the high cost associated with devices used to measure DNI and DHI. Hence, numerous prior works have investigated various solar decomposition models aimed at computing direct and diffuse irradiance from GHI. The present study introduces a novel separation approach for direct and diffuse irradiance, employing machine learning algorithms and utilizing data with a temporal resolution of 1 min. Three machine learning models utilizing the gradient boost technique are suggested and trained using data collected from 10 stations across the world with different climate conditions. The machine learning model called CatBoost outperforms all the solar decomposition models at every station. It achieves the lowest root mean squared error (RMSE) of 8.73% when calculating DNI. The concept of explainable machine learning is further explored through the utilization of shapley additive explanations (SHAP), which allows for the assessment of the significance and interaction of the input parameters. In summary, the results of this study reveal that humidity is an important parameter for the estimation of DNI and DHI.