Enhancing solar power forecasting with machine learning using principal component analysis and diverse statistical indicators

Predicting solar energy is essential for efficient power system planning and the successful integration of renewable energy sources. This study aims to develop a framework for evaluating various machine learning models and feature selection strategies for solar energy prediction. The research applies six machine learning models, i. e. , linear regression (LR), random forest (RF), neural networks (NN), K-nearest neighbor (KNN), gradient boosting (GB), and AdaBoost, to datasets from 2019 to 2021 collected at the Abiod Sid Cheikh solar station in southern Algeria. Various statistical indicators, including R2, RMSE, MAE, and Adj-R, were analyzed to assess model performance. The analysis revealed that R2 values ranged from 0. 591 to 0. 996 kW/m2, RMSE from 0. 510 to 1. 78 kW/m2, and MAE from 0. 357 to 0. 856 kW/m2 across different models. KNN and NN models showed significant errors, while GB and RF models demonstrated strong accuracies (RMS = 0. 9). AdaBoost and LR excelled in real-time and short-term predictions, exhibiting an RMS of 0. 99. This framework offers a comprehensive evaluation method for selecting the most suitable machine learning models and feature selection strategies for solar energy prediction. The findings can assist energy planners and engineers in choosing the appropriate models for accurate solar energy prediction, thereby enhancing the efficiency of power systems. Improved solar energy prediction can contribute to more reliable integration of renewable energy into power grids, supporting the transition to cleaner energy sources and reducing environmental impacts.