SolarRadnet: A novel variant input scoring optimized recurrent neural network for solar irradiance prediction

Abstract

ABSTRACT Solar irradiance prediction is an essential one in providing renewable energy proficiently. The solar irradiance plays a major role in solar power system, solar thermal system, and photovoltaic grid-connected system, owing to uncertainty and variability. Conventional data analysis approaches are complex for demonstrating superior generalization. Therefore, the resource planners are flexible in accommodating these uncertainties while executing planning. To enhance the performance of solar irradiance forecasting, a new Variant Input Scoring Optimized Recurrent Neural Network (VIS-ORNN) is developed. The suggested approach includes two stages that are data collection and three stage simulation. At first, the data are gathered from the various meteorological standard dataset. Then, the prediction begins with feeding data directly to the ORNN. Here, the parameters of RNN are optimized with the help of Adaptive Escaping Energy-based Harris Hawks Coyote Optimization (AEE-HHCO) algorithm. Thus, the first score prediction is obtained. In the second phase, the first order statistical features act as an input, and it is given to the same ORNN, in which the second score is determined. In the third phase, the deep features are extracted by Convolutional Neural Network (CNN) that is subjected to the same ORNN for attaining the score. Finally, the final simulation is determined by taking the average of three prediction models. From the experimental results, while taking the MAE, the suggested AEE-HHCO-ORNN method has correspondingly secured 34.3% enhanced than PSO-ORNN, 7.7% enhanced than WOA-ORNN, 21.7% enhanced than COA-ORNN and 26.5% enhanced than HHO-ORNN. Thus, the simulation outcomes reveal that the offered method ensures maximum accuracy while validating with other baseline methodologies.