  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.
