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

Adam Kisvari, Zi Lin and Xiaolei Liu

Renewable Energy, 2021, vol. 163, issue C, 1895-1909

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.

Keywords: Wind power forecasting; SCADA data; Feature engineering; Deep learning; Offshore wind turbines (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148120316918
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:163:y:2021:i:c:p:1895-1909

DOI: 10.1016/j.renene.2020.10.119

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().