Predicting wind power using LSTM, Transformer, and other techniques

Abstract

Predicting wind turbine energy is essential for optimizing renewable energy utilization and ensuring grid stability. Accurate forecasts enable effective resource planning, minimizing reliance on non-renewable energy sources and reducing carbon emissions. Additionally, precise predictions support efficient grid management, allowing utilities to balance supply and demand in real time, ultimately enhancing energy reliability and sustainability. In this study, we bridge the gap by exploring various machine learning (ML) and deep learning (DL) methodologies to enhance wind power forecasts. We emphasize the importance of accuracy in these predictions, aiming to overcome current standards. Our approach utilized these models to predict wind power generation for the next 15 days, utilizing the SCADA Turkey dataset and Tata Power Poolavadi Data collected. We used R2 scores alongside traditional metrics like mean absolute error (MAE) and root mean square error (RMSE) to evaluate model performance. By employing these methodologies, we aim to enhance wind power forecasting, thereby enabling more efficient utilization of renewable energy resources.

Keywords

• temporal dependencies
• feature engineering
• ensemble methods
• grid integration
• hyperparameter tuning