Predicting Solar Power Based on Machine Learning Approaches and Their Efficacy

The planning, administration, and operation of power systems depend on enhancing the forecasting accuracy of solar energy as it grows in popularity within the worldwide electric grid. However, the erratic and disorganised nature of solar energy data makes this a difficult process. To increase a forecasting accuracy of renewable energy, a number of approaches have been developed to date, including statistical methodologies, physical models, and AI techniques. Using weather data collected in Errachidia, Morocco, this research explores the use of several ML techniques for predicting solar power production. Different models, namely ANN, Ridge Regression, SVM, and KNN were employed to forecast solar energy output. Performance evaluation index; therefore, the R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>_ score and the root mean standard error were used to assess the prowess of the different models. The findings showed that when it came to solar energy predictions, the ANN model was the most accurate, with a R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 99.93 and an RMSE of 0.08. In order to enhance energy management tactics, the results show that ML methods, especially ANN, may increase an accuracy of solar power projections.