Comparative Analysis of Single and Hybrid Machine Learning Models for Daily Solar Radiation

Abstract

This study investigates the estimation of daily solar radiation (SR) through various machine learning (ML) models, including the k-nearest neighbor algorithm (KNN), support vector regression (SVR), and random forest (RF), both individually and in combination with the wavelet transform (WT). The assessment of these models is based on meteorological data spanning three decades (1981-2010) from the province of Kutahya in Turkiye. To address the inherent uncertainty in these data-driven models, the quantile regression method is employed for uncertainty analysis. Statistical metrics, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), mean prediction interval (MPI), and prediction interval coverage probability (PICP), are utilized to evaluate the effectiveness and uncertainties of the models. The SVR and KNN models exhibit comparable performances concerning both predictive accuracy and uncertainty levels. However, hybrid models, such as KNN-WT, RF-WT, and SVR-WT, display enhanced accuracy compared to individual ML models, as indicated by statistical performance criteria. Notably, the SVR-WT model, incorporating inputs such as sunshine duration, air temperature, wind speed, and relative humidity, outperforms other models in terms of RMSE (2.174 MJ/m2), MAE (1.721 MJ/m2), R2 (0.923), MPI (28.55), and PICP (0.80) for the testing dataset. In conclusion, the integration of WT significantly improves the performance of ML models, providing valuable insights for the design and operation of solar energy systems, where precise daily SR estimation is critical for optimal and cost-efficient operation.