Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

Juan Manuel González-Sopeña, Vikram Pakrashi, Bidisha Ghosh

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Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

Short-Term Wind Power Forecasting, Ensemble Empirical Mode Decomposition, Deep Learning, Prediction Intervals, Quantile Regression, Wind Power

Published online , 8 pages
Copyright © 2022 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Juan Manuel González-Sopeña, Vikram Pakrashi, Bidisha Ghosh, Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting, Materials Research Proceedings, Vol. 20, pp 58-65, 2022

DOI: https://doi.org/10.21741/9781644901731-8

The article was published as article 8 of the book Floating Offshore Energy Devices

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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