Analyzing the performance results of models for wind power forecasting

This study aims to investigate the performance results of various models developed for wind energy forecasting with a special focus on improving operational efficiency and ensuring grid stability in the context of increasing renewable energy integration. Against the backdrop of a global transition towards sustainable energy systems, the study highlights the important role of wind energy not only as a significant contributor to renewable energy portfolios but also as a strategic element in reducing fossil fuel dependency. The study centers on the evaluation of ultra-short-term forecasting and multi-step time series forecasting models. A range of methodologies are investigated, including advanced algorithms such as Stacked Denoising Auto Encoders (SDAE), XGBoost and CatBoost, and hybrid models combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU). Another focus of the study is the critical role of feature selection and engineering. Using a consensus-based approach that combines multiple feature selection techniques, the study demonstrates how optimizing input variables can significantly increase forecast accuracy. The integration of various methodological approaches and the combination of statistical, machine learning and deep learning models collectively contribute to a more robust forecasting framework. In conclusion, the findings of this research provide valuable insights into the multifaceted challenges of wind power forecasting and attempt to provide a workable path towards more accurate and reliable forecasting systems. The empirical evidence and methodological innovations presented here carry important practical implications for the management of renewable energy systems