期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2019
卷号：46
期号：4
页码：768-776
出版社：IAENG - International Association of Engineers
摘要：Overuse of non-renewable energy has seriously affected the natural environment. Wind energy is a kind of clean energy with great potential. The higher accuracy of wind speed forecasting, the higher utilization efficiency of wind energy will be. BP neural network can solve nonlinear problems, but it has different generalization ability to different data. Therefore, this paper proposed a forecasting model based on fuzzy c-means clustering (FCM) and improved mind evolutionary algorithm-BP (IMEA-BP). Firstly, the input data set of BP is divided into several classes by FCM, and the number of class is obtained by multiple experiments. After classification, the coefficient of variation in each input vector is used for outlier detection, and outliers are removed from the input data set. Then different IMEA-BP models are built for each class of input data set. Finally, the class of forecasting input is determined and the corresponding IMEA-BP is used for forecasting. The experimental results of two cases showed that the proposed model is not only suitable for one-step forecasting, but also improves the accuracy of multi-step forecasting.
关键词：Overuse of non-renewable energy has seriously affected the natural environment;Wind energy is a kind of clean energy with great potential;The higher accuracy of wind speed forecasting; the higher utilization efficiency of wind energy will be;BP neural network can solve nonlinear problems; but it has different generalization ability to different data;Therefore; this paper proposed a forecasting model based on fuzzy c-means clustering (FCM) and improved mind evolutionary algorithm-BP (IMEA-BP);Firstly; the input data set of BP is divided into several classes by FCM; and the number of class is obtained by multiple experiments;After classification; the coefficient of variation in each input vector is used for outlier detection; and outliers are removed from the input data set;Then different IMEA-BP models are built for each class of input data set;Finally; the class of forecasting input is determined and the corresponding IMEA-BP is used for forecasting;The experimental results of two cases showed that the proposed model is not only suitable for one-step forecasting; but also improves the accuracy of multi-step forecasting.
