Short-term Wind Power Probability Forecasting Method Based on Improved Meteorological Clustering and Classification

被引：0

作者：

Wu H. ^{[1
]}

Sun R. ^{[2
]}

Liao S. ^{[1
]}

Ke D. ^{[1
]}

Xu J. ^{[1
]}

Xu H. ^{[2
]}

机构：

[1] School of Electrical Engineering and Automation, Wuhan University, Wuhan

[2] State Grid Jibei Electric Power Company Limited, Beijing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 15期

关键词：

bald eagle search algorithm; Cornish-Fisher series expansion; Gaussian mixture model clustering; meteorological clustering and classification; probability forecasting; wind power;

D O I：

10.7500/AEPS20210914003

中图分类号：

学科分类号：

摘要：

Accurate wind power forecasting is a critical support for the safe and efficient operation of the renewable-energy-based power system. Aiming at the problem that the existing forecasting methods do not fully consider the difference in wind power output characteristics under different meteorological conditions, a wind power probability forecasting method based on Gaussian mixture model (GMM) clustering with improved bald eagle search (IBES) -expectation maximum (EM) algorithm (IBES-EM-GMM) and meteorological classification is proposed. First, based on Levy flight and adaptive t-distribution mutation strategy, the bald eagle search algorithm is improved, and GMM clustering model based on the IBES-EM algorithm is proposed to enhance the global search ability. Based on this, the IBES-EM-GMM clustering model is applied to cluster the historical weather-power data set, and the hybrid deep neural network along with Cornish-Fisher series is used to train the data sets with different meteorological patterns to obtain their probability forecasting results. Finally, the actual data of wind farms in Jibei, China are selected as an example for simulation. The results show that compared with the methods without clustering and with GMM clustering, the proposed IBES-EM-GMM clustering model leads to a significant improvement in clustering effect and the accuracy of point and probability forecasting for wind power. © 2022 Automation of Electric Power Systems Press. All rights reserved.

引用

页码：56 / 65

页数：9

共 23 条

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