Skip to main content

ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1474969
This article is part of the Research Topic Data-Driven Approaches for Efficient Smart Grid Systems View all 12 articles

A study of short-term wind power segmentation forecasting method considering weather on ramp segments

Provisionally accepted
Chunxiang Yang Chunxiang Yang 1*Guodong Wu Guodong Wu 1*Yongrui Zhang Yongrui Zhang 2*Guangqing Bao Guangqing Bao 3*jianhui Wang jianhui Wang 4
  • 1 State Grid Gansu Electric Power Company, Lanzhou, Gansu Province, China
  • 2 Lanzhou University of Technology, Lanzhou, Gansu Province, China
  • 3 Southwest Petroleum University, Chengdu, Sichuan Province, China
  • 4 Northwest University for Nationalities, Lanzhou, Gansu Province, China

The final, formatted version of the article will be published soon.

    The short-term fluctuation of wind power can affect its prediction accuracy. Thus, a short-term segmentation prediction method of wind power based on ramp segment division is proposed. A time-series trend extraction method based on moving average iteration is proposed on the full-time period to analyze the real-time change characteristics of power time-series initially; secondly, a ramp segment extraction method based on its definition and identification technique is proposed based on the results of the trend extraction; and a segmentation prediction scheme is proposed to lean the power prediction under different time-series: the LightGBM-LSTM is proposed for the non-ramping segment using point prediction, and the CNN-BiGRU-KDE is proposed for probabilistic prediction of ramp segments. From the results, this ramp segment definition and identification technique can effectively identify the ramp process of wind power, which makes up for the misidentification and omission of the classical climbing event definition; meanwhile, the segment prediction scheme not only meets the prediction accuracy requirements of the non-ramping segment, but also provides the effective robust information for the prediction of the ramping period, which offers reliable reference information for the actual wind farms. In particular, it is well adapted to wind power prediction under extreme working conditions caused by ramping weather, which is a useful addition to short-term wind power prediction research.

    Keywords: ramp segment, wind power, Trend identification, probabilistic fitting, segmental prediction

    Received: 02 Aug 2024; Accepted: 25 Sep 2024.

    Copyright: © 2024 Yang, Wu, Zhang, Bao and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Chunxiang Yang, State Grid Gansu Electric Power Company, Lanzhou, Gansu Province, China
    Guodong Wu, State Grid Gansu Electric Power Company, Lanzhou, Gansu Province, China
    Yongrui Zhang, Lanzhou University of Technology, Lanzhou, Gansu Province, China
    Guangqing Bao, Southwest Petroleum University, Chengdu, 610500, Sichuan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.