Deep Learning for Wind Power: Enhancing Prediction Accuracy through High-Resolution Data Reconstruction

As renewable energy, particularly wind power, becomes a cornerstone of global energy strategies, the accuracy of wind prediction models has critical implications for grid stability and economic efficiency. This study introduces a novel deep learning framework designed to significantly enhance the resolution and accuracy of wind data, thereby improving predictive models for wind power generation. Utilizing a combination of high-resolution Numerical Weather Prediction (NWP) data and lower-resolution reanalysis data, our model reconstructs wind data at a scale necessary for effective wind farm planning and operation. Employing advanced techniques such as Fast Fourier Transform (FFT) and Radially Averaged Power Spectral Density (RAPSD), the model analyzes multi-scale variability in wind patterns. This approach allows for a detailed examination of both large-scale atmospheric flows and finer meteorological phenomena—crucial for accurate wind prediction. In the spatial domain, a Uniform Filter segregates fine-scale from broad-scale features, enhancing the model’s ability to capture essential details without losing the context of overarching weather patterns. Generative Adversarial Networks (GANs) are a pivotal component of our methodology. These networks train to model the statistical distribution of wind features with high fidelity, bridging the gap between theoretical accuracy and practical applicability. By integrating stochastic and deterministic training elements, our model balances the randomness inherent in fine-scale wind variability with the necessary coherence of large-scale patterns. Preliminary tests demonstrate that our model achieves a Root Mean Square Error (RMSE) of 1.83 m/s, representing a significant improvement of 0.16 m/s compared to existing meteorological models. When deployed in real-world scenarios, such as a wind farm, the model shows a 23% improvement with a Normalized Mean Absolute Error (NMAE) of 0.17 in wind power prediction, enhancing both the reliability and economic viability of wind energy projects. This study not only advances the technical capabilities of wind data modeling but also provides a robust framework for the practical application of these improvements in wind power prediction. The deep learning approach outlined here holds considerable promise for transforming wind energy management and deployment, setting a new standard for precision in renewable energy technologies.

Keywords: Wind Data Downscaling, Multi-Scale Integration, Meteorological Gridded Data, Deep Learning, Wind Energy Management, Wind Farm Development