Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness

TL;DR

This paper introduces a novel shape-wise feature engineering method that improves wind speed and power forecasting accuracy and robustness by modifying data input shapes in CNN-LSTM and autoregressive models, effectively handling data noise.

Contribution

The study presents a new shape-wise feature engineering technique that enhances deep learning models' resilience to noise in wind forecasting tasks, outperforming traditional approaches.

Findings

Achieved up to 83% accuracy in 24-step ahead predictions.

Enhanced model robustness against data noise.

Consistent high accuracy across short, mid, and long-term forecasts.

Abstract

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.