Xiaojuan Han, Fangyuan Meng, Zhihui Song, Xiangjun Li
{"title":"结合GM(1,1)模型和云模型的风电短期预测","authors":"Xiaojuan Han, Fangyuan Meng, Zhihui Song, Xiangjun Li","doi":"10.1109/ICAL.2012.6308195","DOIUrl":null,"url":null,"abstract":"This paper proposes a new method to predict wind power of wind farm using the combination of GM(1,1) model and cloud model. The original wind power signals are decomposed into high frequency part and low frequency part by wavelet decomposition. Cloud model is constructed to predict wind power of high frequency part and GM(1,1) model is used to predict wind power of low frequency part. The predicted power can be obtained by high frequency part and low frequency part. The simulation example shows that the method proposed in this paper is obviously better than single predicting method and the effectiveness of the method is verified by the predicting results.","PeriodicalId":373152,"journal":{"name":"2012 IEEE International Conference on Automation and Logistics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Short-term prediction of wind power combining GM(1,1) model with cloud model\",\"authors\":\"Xiaojuan Han, Fangyuan Meng, Zhihui Song, Xiangjun Li\",\"doi\":\"10.1109/ICAL.2012.6308195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new method to predict wind power of wind farm using the combination of GM(1,1) model and cloud model. The original wind power signals are decomposed into high frequency part and low frequency part by wavelet decomposition. Cloud model is constructed to predict wind power of high frequency part and GM(1,1) model is used to predict wind power of low frequency part. The predicted power can be obtained by high frequency part and low frequency part. The simulation example shows that the method proposed in this paper is obviously better than single predicting method and the effectiveness of the method is verified by the predicting results.\",\"PeriodicalId\":373152,\"journal\":{\"name\":\"2012 IEEE International Conference on Automation and Logistics\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Automation and Logistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAL.2012.6308195\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Automation and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAL.2012.6308195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short-term prediction of wind power combining GM(1,1) model with cloud model
This paper proposes a new method to predict wind power of wind farm using the combination of GM(1,1) model and cloud model. The original wind power signals are decomposed into high frequency part and low frequency part by wavelet decomposition. Cloud model is constructed to predict wind power of high frequency part and GM(1,1) model is used to predict wind power of low frequency part. The predicted power can be obtained by high frequency part and low frequency part. The simulation example shows that the method proposed in this paper is obviously better than single predicting method and the effectiveness of the method is verified by the predicting results.