Wind Power Forecasting with Support Vector Machines using Sparrow Search Algorithm

An efficient wind power forecasting system is essential to maximize the benefits of renewable energy’s incorporation into the grid. Thus, a new method for wind power forecasting by combining the Sparrow Search Algorithm (SSA) with Support Vector Machines (SVM) is presented. In order to fine-tune the settings of the SVMs, the SSA is employed, which uses the hunting behavior of sparrows. It improves the model’s capacity to capture fine fluctuations in wind power production. The proposed SSA-SVM model is tested using historical wind power data, and the findings show a considerable increase in predicting accuracy compared to standard SVM models. Time series analysis also shows how well the SSA-SVM model can adjust to wind power patterns. The effectiveness of the proposed system for wind power forecasting and its potential role in enhancing the prediction of wind power in the renewable energy environment are analyzed.