A Decomposition-Based Deep Learning Approach for Wind Speed Forecasting Using CEEMDAN and LSTM

Rakesh Yadav G.; Ramu V

doi:10.63363/aijfr.2026.v07i03.5567

A Decomposition-Based Deep Learning Approach for Wind Speed Forecasting Using CEEMDAN and LSTM

Author(s)	Mr. Rakesh Yadav G., Dr. Ramu V
Country	India
Abstract	Accurate prediction of wind speed is crucial for the reliable operation of wind energy systems and the stability of modern power grids. Nevertheless, wind speed data are highly nonlinear, non-stationary, and influenced by various environmental factors, making precise forecasting a challenging task. To address these challenges, this study proposes a hybrid deep learning framework that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Long Short-Term Memory (LSTM) neural network for short-term wind speed prediction. In the proposed approach, the CEEMDAN technique is first employed to decompose the original wind speed time series into a finite number of intrinsic mode functions (IMFs), each representing different frequency components and hidden patterns within the data. This decomposition reduces noise and enhances the interpretability of complex temporal structures. Subsequently, each IMF sub-series is individually modelled using LSTM networks, which are well-suited for capturing long-term dependencies and nonlinear relationships in time-series data. The outputs of these individual models are then aggregated to produce the final wind speed forecast. The effectiveness of the proposed hybrid model is validated using real-time wind speed data obtained from the National Institute of Wind Energy (NIWE), India. Performance evaluation is carried out using standard statistical error metrics, demonstrating that the proposed CEEMDAN–LSTM model significantly outperforms conventional and standalone models in terms of prediction accuracy. The results confirm that the developed approach provides a robust and efficient solution for short-term wind speed forecasting in practical renewable energy applications.
Keywords	Renewable energy systems, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), Deep neural network, short-term forecasting, Wind speed prediction
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2026
Published On	2026-05-07
DOI	https://doi.org/10.63363/aijfr.2026.v07i03.5567

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A Decomposition-Based Deep Learning Approach for Wind Speed Forecasting Using CEEMDAN and LSTM

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