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

RENEWABLE ENERGY

卷:163

Article

Kisvari, Adam¹  Lin, Zi²  Liu, Xiaolei¹ 

[1] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland

[2] Northumbria Univ, Dept Mech & Construct Engn, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England

关键词: Wind power forecasting;    SCADA data;    Feature engineering;    Deep learning;    Offshore wind turbines;   

DOI  :  10.1016/j.renene.2020.10.119

来源: Elsevier

【 摘 要 】

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. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

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