全球能源互联网2026，Vol.9Issue(1)：24-35,12.DOI:10.19705/j.cnki.issn2096-5125.20240452
基于模糊聚类与Copula的场景特征自适应风速预测模型
A Scenario-adaptive Wind Speed Prediction Model Based on Fuzzy Clustering and Copula Functions
摘要
Abstract
Aiming at the problems of complex feature selection,low computational efficiency,and insufficient model generalization ability in multivariate wind speed forecasting,this paper proposes an adaptive wind speed forecasting model integrating scenario division and optimal Copula selection.A three-stage collaborative mechanism of"scenario clustering-dynamic variable selection-rolling forecasting"is constructed.First,the multidimensional meteorological data are divided into weather scenarios with similar characteristics using the fuzzy C-means clustering algorithm.Second,a multivariate correlation model is constructed using the Copula function,and the optimal Copula function is selected based on the Euclidean distance.Combined with the comprehensive correlation coefficient,scenario-adaptive dynamic variable selection is realized.Finally,a scenario-based LSTM forecasting model and a real-time data rolling update strategy are designed.The prediction accuracy is improved by dynamically matching the scenario characteristics with the forecasting model.Verification using publicly available weather data from a region in Europe shows that the proposed method outperforms single-scenario forecasting models in terms of wind speed forecasting accuracy.Specifically,the root mean square error is reduced by 3.6%,the normalized error is reduced by 5.2%,the mean absolute percentage error is reduced by 4.2%,and the coefficient of determination is increased by 4.5%.关键词
风速预测/长短期记忆网络(LSTM)/Copula函数场景自适应/模糊C均值聚类Key words
wind speed forecasting/long short-term memory(LSTM)network/Copula function-based scenario adaptation/fuzzy C-Means clustering分类
信息技术与安全科学引用本文复制引用
王永真,唐豪,韩特,李嘉宇,韩恺,冶兆年..基于模糊聚类与Copula的场景特征自适应风速预测模型[J].全球能源互联网,2026,9(1):24-35,12.基金项目
国家自然科学基金项目(52006114). National Natural Science Foundation of China(52006114). （52006114）
