Improving Wind Power Forecasting Using Hybrid Ensemble Methods

Accurate prediction of wind power will maintain grid stability by efficiently managing energy. To this end, this paper proposes a state-of-the-art hybrid model that incorporates machine learning into deep learning techniques to better carry out short-term wind power forecasting. The proposed model incorporates GRUs for temporal dependency, followed by the application of CNNs to identify spatial features, and lastly refines using SVMs for post-processing corrections. The use of an attention mechanism that dynamically focuses on the most relevant historical data chunks would improve predictive accuracy in this model. The ensemble approach thus reduces the error of predictions and enhances robustness by training several instances of the model on several subsets of data containing meteorological information. Testing this hybrid architecture on an extensive dataset from a wind farm yields results that show this architecture indeed provides an effective and useful solution to forecasting while being able to capture subtle patterns that exist in the wind power data. It enables the integration of renewable energy sources into power networks more effectively and pushes for more environmentally friendly ways to manage energy.