WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

Abstract

Solar energy is one of the most important renewable energy sources. Assessing the solar potential of area needs analyzed information about the dataset of the measured global solar radiation (GSR). Recently, researches detected the high potential of state-of-the-art artificial intelligence (AI) methods in estimating the GSR successfully. In this study, a novel hybrid AI-based tool consisting of least square support vector machine (LSSVM) integrated with improved simulated annealing (ISA) is proposed to predict the GSR over the Ahvaz synoptic station located in the South-West of Iran. The potential of the proposed hybrid paradigm so-called LSSVM-ISA was evaluated by using multivariate adaptive regression spline (MARS), generalization regression neural network (GRNN), and multivariate linear regression with interactions (MLRI). For precise assessment of efficiency of the AI models, various statistical metrics and validation methods were used to assess the precision of the developed models. A comparison of the obtained results indicated that the LSSVM-ISA method performed better than the MARS, GRNN, and MLRI models. The achieved RMSE values of the MARS, GRNN, and MLRI models were decreased by 9%, 16%, and 30% using the LSSVM-ISA model. Finally, the results demonstrated that the LSSVM-ISA model could be successfully employed for accurately predicting GSR.