Offshore wind power forecasting-A new hyperparameter optimisation algorithm for deep learning models

Hanifi, S., Lotfian, S., Zare-Behtash, H. and Cammarano, A. (2022) Offshore wind power forecasting-A new hyperparameter optimisation algorithm for deep learning models. Energies, 15(19), 6919. (doi: 10.3390/en15196919)

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The main obstacle against the penetration of wind power into the power grid is its high variability in terms of wind speed fluctuations. Accurate power forecasting, while making maintenance more efficient, leads to the profit maximisation of power traders, whether for a wind turbine or a wind farm. Machine learning (ML) models are recognised as an accurate and fast method of wind power prediction, but their accuracy depends on the selection of the correct hyperparameters. The incorrect choice of hyperparameters will make it impossible to extract the maximum performance of the ML models, which is attributed to the weakness of the forecasting models. This paper uses a novel optimisation algorithm to tune the long short-term memory (LSTM) model for short-term wind power forecasting. The proposed method improves the power prediction accuracy and accelerates the optimisation process. Historical power data of an offshore wind turbine in Scotland is utilised to validate the proposed method and compare its outcome with regular ML models tuned by grid search. The results revealed the significant effect of the optimisation algorithm on the forecasting models’ performance, with improvements of the RMSE of 7.89, 5.9, and 2.65 percent, compared to the persistence and conventional grid search-tuned Auto-Regressive Integrated Moving Average (ARIMA) and LSTM models.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zare-Behtash, Dr Hossein and Hanifi, Mr Shahram and Cammarano, Dr Andrea
Creator Roles:
Hanifi, S.Conceptualization, Methodology, Writing – original draft, Writing – review and editing, Data curation
Zare-Behtash, H.Writing – review and editing
Cammarano, A.Writing – review and editing
Authors: Hanifi, S., Lotfian, S., Zare-Behtash, H., and Cammarano, A.
College/School: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:Energies
ISSN (Online):1996-1073
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Energies 15(19):6919
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
305200DTP 2018-19 University of GlasgowMary Beth KneafseyEngineering and Physical Sciences Research Council (EPSRC)EP/R513222/1MVLS - Graduate School