Wind power forecasting – a data-driven method along with gated recurrent neural network

Kisvari, A., Lin, Z. and Liu, X. (2021) Wind power forecasting – a data-driven method along with gated recurrent neural network. Renewable Energy, 163, pp. 1895-1909. (doi: 10.1016/j.renene.2020.10.119)

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Effective wind power prediction will facilitate the world’s long-term goal in sustainable development. However, a drawback of wind as an energy source lies in its high variability, resulting in a challenging study in wind power forecasting. To solve this issue, a novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time. Besides, a novel deep learning neural network of Gated Recurrent Unit (GRU) is successfully developed and critically compared with the algorithm of Long Short-term Memory (LSTM). Initially, twelve features were engineered into the predictive model, which are wind speeds at four different heights, generator temperature, and gearbox temperature. The simulation results showed that, in terms of wind power forecasting, the proposed approach can capture a high degree of accuracy at lower computational costs. It can also be concluded that GRU outperformed LSTM in predictive accuracy under all observed tests, which provided faster training process and less sensitivity to noise in the used Supervisory Control and Data Acquisition (SCADA) datasets.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Liu, Dr Xiaolei
Creator Roles:
Liu, X.Conceptualization, Resources, Investigation, Data curation, Writing – review and editing, Supervision
Authors: Kisvari, A., Lin, Z., and Liu, X.
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Renewable Energy
ISSN (Online):1879-0682
Published Online:28 October 2020
Copyright Holders:Copyright © 2020 Elsevier Ltd.
First Published:First published in Renewable Energy 163: 1895-1909
Publisher Policy:Reproduced under a Creative Commons License

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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