An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division

Anbo Meng, Haitao Zhang, Zhongfu Dai, Zikang Xian, Liexi Xiao, Jiayu Rong, Chen Li, Jianbin Zhu, Hanhong Li, Yiding Yin, Jiawei Liu, Yanshu Tang, Bin Zhang and Hao Yin

Energy, 2024, vol. 299, issue C

Abstract: Precise wind power prediction (WPP) can address the issue caused by large-scale wind power grid integration to the power system operation. Most WPP research focus on the randomness and high volatility problem of wind power but ignore the time-series distribution shift (TSDS) problem. To solve the TSDS issue, this study proposes a novel hybrid model that incorporates complementary ensemble empirical mode decomposition (CEEMD), time-series distribution period division (TSDPD) and adaptive distribution-matched GRU (ADMGRU). First, CEEMD is utilized to decompose the nonstationary data into in sub-sequence, reducing complexity and randomness. Second, TSDPD is employed to automatically identify the underlying temporal segments within wind power sequence by maximizing discrepancies in distribution information between two periods, determining the quantity and respective boundaries of periods. Finally, ADMGRU, comprising Pre-train and Boosting-based importance assessment components, learns prediction model accurately by dynamically matching distribution periods. The former component initializes predictive model parameters and the latter learns the importance of each hidden state to assigns corresponding weights to different distributions. Numerous comparative experiments demonstrate the CEEMD-TSDPD-ADMGRU hybrid model surpasses existing popular models. Especially in spring scenario, compared with other four advanced models, the maximum reduction in MAE and RMSE are 54.64 % and 73.33 %, respectively.

Keywords: Wind power prediction; Time-series distribution shift; Time-series distribution period division; Adaptive distribution-matched network; Complementary ensemble empirical mode decomposition (search for similar items in EconPapers)
Date: 2024
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/S0360544224011563
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:299:y:2024:i:c:s0360544224011563

DOI: 10.1016/j.energy.2024.131383

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 ().