Optimized Forecasting Approach for Scheduling Wind Generation Plants and Maximizing Renewable Energy Utilization

Abstract

This study focuses on optimizing wind generation plant scheduling to enhance renewable energy utilization while minimizing costs and ensuring grid stability. An advanced forecasting approach is proposed to predict wind generation plant availability for future intervals, integrating accurate demand prediction with renewable energy forecasting. Evaluation metrics like Mean Error (ME), Root Mean Square Error (RMSE), and Correlation assess the proposed solution’s performance, showing its superiority over baseline models with an RMSE of 9.39. Results highlight the effectiveness of the approach in forecasting wind generation plant availability, essential for efficient renewable energy utilization. By reducing reliance on traditional energy sources, the method contributes to cost minimization and promotes sustainability. The study underscores the potential of advanced forecasting techniques in optimizing renewable energy scheduling, offering opportunities for grid stability and reduced environmental impact. The proposed method involves training an LSTM model on preprocessed wind turbine data, integrating it with a scheduling algorithm for optimized task scheduling, and continuously learning to improve efficiency. The Optimized LSTM model outperforms other models in wind power forecasting, providing accurate predictions crucial for energy management decisions. Integration with SCADA control and maintenance scheduling further enhances operational efficiency and cost reduction in wind turbine management.