太原理工大学学报2018，Vol.49Issue(1)：133-139,7.DOI:10.16355/j.cnki.issn1007-9432tyut.2018.01.020
基于模糊聚类分析的风电功率预测研究
Research on Wind Power Forecasting Based on Fuzzy Clustering Analysis
摘要
Abstract
Improving short-term wind power forecasting accuracy is an urgent requirement for the development of large scale wind power,and it is also the key to ensuring the integrated operation of wind power.In this paper,a method to improve the forecasting accuracy based on clustering is proposed without increasing the complexity of the model.First,training samples are processed by the fuzzy C-means clustering method optimized by subtractive clustering.Then the forecasting model base corresponding to different data set is established.Finally,different forecasting data are matched to the data in the clustered data set so that the optimized model is selected for wind power forecasting.A lot of actual data of wind farm in Shanxi province are used for simulation study.The results show that it can reduce the number of large prediction error so as to effectively improve the forecasting accuracy of wind power.关键词
风电功率预测/模糊C均值聚类/神经网络/训练样本处理/减法聚类Key words
Wind power forecasting/Fuzzy clustering/Neural network models/Training sample processing/Subtractive clustering分类
信息技术与安全科学引用本文复制引用
张传辉,田建艳,高炜,王芳..基于模糊聚类分析的风电功率预测研究[J].太原理工大学学报,2018,49(1):133-139,7.基金项目
国家自然科学基金资助项目(51277127) （51277127）
