Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting
Juan Manuel González-Sopeña, Vikram Pakrashi, Bidisha Ghosh
download PDFAbstract. 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.
Keywords
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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