Offshore wind power forecasting based on WPD and optimised deep learning methods

Hanifi, S., Zare-Behtash, H. , Cammarano, A. and Lotfian, S. (2023) Offshore wind power forecasting based on WPD and optimised deep learning methods. Renewable Energy, 218, 119241. (doi: 10.1016/j.renene.2023.119241)

Abstract

Accurate wind power forecasting is vital for (i) wind power management, (ii) penetration increment of the power generated into the power grid, and (iii) making maintenance more efficient. Motivated by the recent application of wavelet transforms and advancements in deep learning methods, a hybrid forecasting method is developed based on the wavelet packet decomposition [WPD], Long Short-Term Memory Network [LSTM], and Convolutional Neural Network [CNN] to improve the accuracy of wind power forecasting. WPD is employed to decompose pre-processed wind power data into sublayers with different frequencies. Sequential Model-Based optimisation (SMBO) with the Tree Parzen Estimator (TPE) is then used to tune the hyper-parameters of LSTM and CNN, efficiently. The optimised LSTM is employed to predict the low-frequency sub-layer that has both long-term and short-term dependencies, and CNN is used to forecast the high-frequency sub-layers with short-term dependencies. To evaluate the prediction performance of the developed method, seven forecasting models, including random forest (RF), feed-forward neural network (FFNN), CNN, LSTM, WPD-FFNN, WPD-CNN, and WPD-LSTM models, are considered as comparison models. Comparing the prediction results of all involved models proves that the developed model improves the prediction accuracy by at least 77.4% compared to methods that do not use WPD. In addition, the proposed combination of optimised CNN and LSTM improves the forecasting accuracy by 26.25% compared to methods that use only one deep learning model to forecast all sub-series.

• Item Type: Articles (Editorial)
• Additional Information: This research was supported by the EPSRC Doctoral Training Partnership (EP/R513222/1). The authors thank the Offshore Renewable Energy (ORE) Catapult for provisions of the SCADA database.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zare-Behtash, Dr Hossein and hanifi, shahram and Cammarano, Dr Andrea

Creator Roles

Hanifi, S.: Conceptualization, Methodology, Software, Investigation, Validation, Formal analysis, Writing – original draft, Visualization
Zare-Behtash, H.: Writing – review and editing, Methodology, Supervision
Cammarano, A.: Writing – review and editing, Methodology, Supervision

• Authors: Hanifi, S., Zare-Behtash, H., Cammarano, A., and Lotfian, S.
• Subjects: Q Science > Q Science (General); Q Science > QC Physics
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Renewable Energy
• Publisher: Elsevier
• ISSN: 0960-1481
• ISSN (Online): 1879-0682
• Copyright Holders: Copyright: © 2023 The Authors
• First Published: First published in Renewable Energy 218: 119241
• Publisher Policy: Reproduced under a Creative Commons licence

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
305200		DTP 2018-19 University of Glasgow	Mary Beth Kneafsey	Engineering and Physical Sciences Research Council (EPSRC)	EP/R513222/1	MVLS - Education Hub