Arithmetic optimization with hybrid deep learning algorithm based solar radiation prediction model

Abstract

Solar radiation affects extreme weather occurrences, as well as on the temperature and mean sea level. Therefore, precise studies and measurements of geographical and temporal variations of solar radiation are required. The development of deep learning and machine learning methods for developing solar radiation predictive models is gaining traction. Therefore, this paper presents an Arithmetic Optimization with Hybrid Deep Learning based Solar Radiation Prediction model. The presented AOHDL-SRP model follows a three-stage process: pre-processing, prediction, and hyperparameter optimization. Primarily, the AOHDL-SRP model involves the min–max normalization technique to normalize the input data to a uniform format. Besides, the presented AOHDL-SRP model applies HDL using a convolutional neural network with an attention-oriented long short-term memory (ALSTM) model. Finally, the arithmetic optimization algorithm (AOA) is applied for the hyperparameter optimization of the HDL model, and it assists in improving the predictive performance. The experimental validation of the AOHDL-SRP model is tested, and the results are lower than different expectations. The AOHDL-SRP model has demonstrated that it is superior to all other models by achieving the highest possible R2-score of 100% and MSE, RMSE, and MAE values of 0.18, 0.43, and 0.32, respectively.