DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting

Mingwei Zhong, Cancheng Xu, Zikang Xian, Guanglin He, Yanpeng Zhai, Yongwang Zhou and Jingmin Fan

Energy, 2024, vol. 286, issue C

Abstract: Accurate wind power forecasting (WPF) is vital for grid stability. Most existing studies rely on the combination methods, and the multi-source information (MSI) related to the wind power, but rarely consider the model performances in online prediction scenarios, where an efficient model structure and the timeliness of MSI are worth considering. In this study, a deep temporal transfer model (DTTM) and a synchronous training strategy (STS) are initially introduced. Firstly, temporal synchronous data is mapped to external temporal data (ETD), not only avoiding the asynchronous obtaining time of the MSI, but also making the past and future spatial-temporal information relevant. Secondly, to expand the forecasting model structure for adapting the ETD, a lightweight model named parallel network (PN) is developed as a forecasting cell. Based on deep parallel network (DPN), to fully train ETD for acquiring superior online prediction effect, a highway convolutional neural network (HCNN) is utilized to link the ETD to DPN, mitigating the degradation caused by insufficient information transmission. Through applying DTTM structure adopted STS, the Normalized Mean Absolute Error (NMAE) reduces by up to 74.42 % and at least 38.11 % compared with other models, achieving better online WPF performance.

Keywords: Online wind power forecasting; External temporal data; Parallel network; Synchronous training strategy; Deep temporal transfer model (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223029821
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:286:y:2024:i:c:s0360544223029821

DOI: 10.1016/j.energy.2023.129588

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 ().