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

Kisvari, Adam, Lin, Zi and Liu, Xiaolei (2021) Wind power forecasting – A data-driven method along with gated recurrent neural network. Renewable Energy, 163. pp. 1895-1909. ISSN 0960-1481

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Official URL: https://doi.org/10.1016/j.renene.2020.10.119

Abstract

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: Article
Uncontrolled Keywords: Wind power forecasting, SCADA data, Feature engineering, Deep learning, Offshore wind turbines
Subjects: H300 Mechanical Engineering
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 04 Nov 2020 14:49
Last Modified: 05 Nov 2020 09:15
URI: http://nrl.northumbria.ac.uk/id/eprint/44685

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