Enhancing Solar Power Forecasting using Grasshopper optimization and Whale Optimization Algorithm

Abstract

Solar power forecasting is essential for effectively integrating solar energy into the power system. Accurate forecasting allows for more effective power system planning, operation, and management. In this research work, we employed the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA) to choose features for solar power forecasting utilizing time series data from the OPSD dataset. The dataset contains measurements taken at 15-minute intervals, giving a wealth of data for training and verifying forecasting algorithms. The WOA is used to adjust the parameters of a forecasting model, hence increasing its accuracy and reliability. The suggested approach's performance is evaluated on a large-scale dataset, with training, validation, and test sets of 100,000, 50,000, and 51,347 data points, respectively. The results show that the WOA is effective at improving solar power forecasting accuracy, which contributes to the efficient use of renewable energy resources.