A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions

Chuanjin Yu, Yongle Li, Liyang Zhao, Qian Chen and Yuxing Xun

Energy, 2023, vol. 262, issue PB

Abstract: For the sensible and efficient use of wind energy, accurate wind speed forecast is crucial. To improve the accuracy of short-term wind speed prediction, a novel recurrent neural network known as the time-frequency recurrent neural network, or TFR for short, is developed. The wavelet transformation is naturally incorporated into the TFR architecture in order to mine the time-frequency characteristics. Additionally, the convolution processes are combined to extract the inherent correlation of time series, enhancing the TFR's performance and creating an advanced model known as CNN-TFR. The prediction ability, parameter sensitivity, and training time of the suggested models for multi-step wind speed forecasts are examined using the wealth of wind speed data from a genuine observation site. It is found that TFR offers greater prediction performance as compared to conventional recurrent neural networks since it can access frequency domain knowledge. Additionally, CNN-TFR's prediction performance has been further improved, making it superior to other CNN based models. For the proposed CNN-TFR model, its sensitivity to input length and wavelet parameters is investigated. It has been shown that with little training time, the CNN-TFR model with strong and robust prediction ability can be utilized to anticipate real wind speed.

Keywords: Wind speed prediction; Recurrent neural network; Time-frequency characteristic; Wavelet transformation; Convolution operations (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222024422
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:energy:v:262:y:2023:i:pb:s0360544222024422

DOI: 10.1016/j.energy.2022.125556

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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