Predicting wind power generation using hybrid deep learning with optimization

Article

Hossain, Md Alamgir, Chakrabortty, Ripon K, ElSawah, Sondoss, Gray, Evan MacA. and Ryan, Michael J.. 2021. "Predicting wind power generation using hybrid deep learning with optimization." IEEE Transactions on Applied Superconductivity. 31 (8). https://doi.org/10.1109/TASC.2021.3091116
Article Title

Predicting wind power generation using hybrid deep learning with optimization

ERA Journal ID4442
Article CategoryArticle
AuthorsHossain, Md Alamgir, Chakrabortty, Ripon K, ElSawah, Sondoss, Gray, Evan MacA. and Ryan, Michael J.
Journal TitleIEEE Transactions on Applied Superconductivity
Journal Citation31 (8)
Article Number0601305
Number of Pages5
Year2021
Place of PublicationUnited States
ISSN1051-8223
1558-2515
Digital Object Identifier (DOI)https://doi.org/10.1109/TASC.2021.3091116
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/9462413
Abstract

Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco Rock Wind Farm in Australia. The model consists of a convolutional neural network, gated recurrent units (GRU) and a fully connected neural network. To improve performance, the hyper-parameters of the model are tuned using the Harris Hawks Optimization algorithm. The effectiveness of the proposed model is evaluated against other advanced models, including multilayer feedforward neural network (NN), recurrent neural network (RNN), long short-term memory (LSTM) and GRU. The forecasting model demonstrates around 38% and 24% higher accuracy as compared to the 5- and 10-min forecasting of the NN model, respectively.

KeywordsVery-short term prediction; wind power generation; hybrid deep learning; optimization algorithm; Boco Rock Wind Far
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204008. Electrical engineering
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Byline AffiliationsGriffith University
University of New South Wales
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