Performance Comparison of Forecasting on Solar Plant Generation Data - Kaur, Sukhpal; Rakshit, Madhuchanda (International Journal of Computer Sciences and Engineering, 2025)
The purpose of this paper is to compare the forecasting performances of daily generation of a solar plant by utilizing the autoregressive time series models. As the demand for energy is increasing frequently all over the world, the proper integration of solar energy and its accurate predictions become necessary for our society for better planning and distribution of energy. In this study, we compare our solar energy time series data with Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) and Vector Autoregressive (VAR) time series models for analyzing our solar plant data separately and at last conclusion is made on the better performance of these two methods. Moreover for VAR model effects of various variables are tested for maximum production of solar power. For evaluating the accuracy performance of our forecasted data, we use Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE) and Root Mean Square Error (RMSE) measurements