Enhancing Wind Power Generation Forecasting with Advanced Deep Learning Technique using Wavelet-Enhanced Recurrent Neural Network and Gated Linear Units

Abstract

Wind power generation forecasting is a critical facet of efficient renewable energy management. This research presents a pioneering approach, the "Wavelet-Enhanced Recurrent Neural Network with Gated Linear Units" (W-RNN-GLU), designed to elevate the precision and insight of wind power forecasting. The model integrates wavelet transformation, recurrent neural networks (RNNs), and Gated Linear Units (GLUs) to capture intricate temporal dependencies and extract relevant feature s from wind power

data. Through multiscale insights facilitated by wavelet transformation, the W-RNN-GLU model discerns fine-grained details and overarching trends. The RNN component adeptly navigates dynamic temporal dependencies, while GLUs regulate feature extraction with precision.

Empirical  evaluations demonstrate the model's superiority, achieving significantly improved forecast accuracy compared to traditional techniques. The proposed model stands as a trailblazing solution, bridging the gap between traditional time series methods and advanced machine learning algorithms. As renewable energy assumes greater prominence, the W-RNN-GLU model emerges as a pivotal tool in shaping the future of wind power generation forecasting.

The effectiveness of the proposed W-RNN-GLU model is substantiated through rigorous empirical evaluations. In comparison to established methods such as Lasso and LightGBM, the W-RNN-GLU model showcases remarkable performance. For instance, the Mean Absolute Error (MAE) achieved by the W-RNN-GLU model is significantly lower than that of Lasso and LightGBM, signifying its enhanced predictive accuracy. Moreover, the Root Mean Square Error (RMSE) achieved by the W-RNN-GLU model underscores its ability to capture nuanced variations within wind power data. This tangible improvement in forecast accuracy positions the W-RNN-GLU model as a transformative solution for wind power generation forecasting, paving the way for more efficient and sustainable energy managementpractices.