Wind farm power forecasting by physical data fusion

ZHAO Jun; ZHANG Shifeng; SONG Jinge

doi:10.11930/j.issn.1004-9649.202510007

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Wind farm power forecasting by physical data fusion

更新时间：2026-08-04

- Wind farm power forecasting by physical data fusion
- Vol. 59, Issue 5, Pages: 176-182(2026)
- 作者机构：
  
  国网山西省电力公司电力科学研究院,山西,太原,030001
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202510007
  CLC：
- Online First：15 May 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
赵军, 张世锋, 宋金鸽. 物理数据融合的风电场功率预测[J]. 中国电力, 2026, 59(5): 176-182.

ZHAO Jun, ZHANG Shifeng, SONG Jinge. Wind farm power forecasting by physical data fusion[J]. 2026, 59(5): 176-182.
赵军, 张世锋, 宋金鸽. 物理数据融合的风电场功率预测[J]. 中国电力, 2026, 59(5): 176-182. DOI： 10.11930/j.issn.1004-9649.202510007.

ZHAO Jun, ZHANG Shifeng, SONG Jinge. Wind farm power forecasting by physical data fusion[J]. 2026, 59(5): 176-182. DOI： 10.11930/j.issn.1004-9649.202510007.

摘要

现有风电功率预测大多依赖数据驱动或物理驱动的单一方法，少有研究将物理模型与数据驱动相结合，而这两种方法之间存在显著的互补潜力。建立了基于

均值聚类、经验模态分解与并行加权长短期记忆网络的数据驱动模型，并构建了融合物理驱动与数据驱动的风电场预测方法。以山西某风电场的实测数据为案例进行验证，所提物理数据融合方法的预测精度比纯数据驱动方法高21.67%，比基于经验尾流物理模型驱动方法高35.17%。该结果证实了物理数据融合方法在风电场功率预测中具有一定优越性及可靠性，能够满足风电场功率预测精度的要求。

Abstract

Power forecasting is a fundamental research topic in the wind power industry. Existing wind power forecasting methods predominantly rely on either data-driven or physics-driven approaches

with few studies combining physical models and data-driven techniques despite their significant complementary potential. A data-driven model is established using

-means clustering

empirical mode decomposition

and parallel weighted long short-term memory networks. A novel integrated approach combining physics-driven and data-driven methods was developed for wind farm forecasting. Validation using real-world data from a Chinese wind farm demonstrated that the proposed integrated method achieved 21.67% higher prediction accuracy than data-driven methods and 35.17% higher accuracy than physics-driven methods. These results confirm the superiority and reliability of physics-data fusi

on methods in wind farm ultra-short power forecasting.

关键词

Keywords

references

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