Improving solar power forecast from meteorological parameters with regression models

Abstract

ABSTRACT Solar power forecasting is crucial in boosting the rivalry of solar power plants in the electricity markets and minimizing economic and social dependence on fossil fuels. This paper describes a method for foretelling solar power by utilizing regression approaches. The proposed regression models are developed using an actual meteorological set of data from Qassim University, KSA for one year (September 2021- August 2022). Forecasting solar power production is crucial to dealing with the smart grid’s demand and supply challenges. This research aims to make ML models that can precisely forecast solar power production. Significantly, the following noteworthy components highlight the main contributions of this work. Primarily a framework for the analysis of data is presented, and then the dataset obtained from the solar photovoltaic (SPV) system is visualized. Secondly, we evaluate the predictive capability of machine learning (ML) models employing numerous performance indices for predicting solar power time-series dataset values. According to the experimental findings, the proposed predictive approaches can lessen forecasting complexity even with a small reconstruction error. Additionally, the GBR (Gradient Boosting regression) model significantly outperformed other benchmark techniques in terms of forecast accuracy with MAPE of 0.7674, RMSE of 0.0191, MAE of 0.0132, MSE of 0.0030, and R2 of 0.9723 respectively.