Performance Evaluation of Various Solar Forecasting Models for Structural & Endogenous Datasets

Abstract

The forecasting of solar irradiation with high precision is critical for fulfilling electricity demand. The dataset used to train the learning-based models has a direct impact on the model’s prediction accuracy. This work evaluates the impact of two types of datasets: structural and endogenous datasets over the prediction accuracy of different solar forecasting models (five variants of artificial neural network (ANN) based models, Support vector machine (SVM), Linear Regression, Bagged and Boosted Regression tree). The issue of variability estimation is also explored in the paper in order to choose the best model for a given dataset. The performance of the models is assessed using two essential error metrics: mean absolute percentage error (MAPE) and root mean square error (RMSE). The results shows that the MAPE and RMSE for structural data vary from 1.99% to 29.73% and 23.39 W/m2 to 165.21 W/m2, respectively, whereas these errors for endogenous dataset ranges from 1.98% to 31.19% and 23.64 W/m22 to 152.56 W/m22. Moreover, these findings, together with the data variability findings, suggest that SVM is the best model for all forms of data variability, whereas CFNN may be employed for greater variability.