{"title":"用ARIMA模式预报短期风速","authors":"V. Radziukynas, A. Klementavicius","doi":"10.1109/RTUCON.2014.6998223","DOIUrl":null,"url":null,"abstract":"The paper deals with the short-term forecasting of wind speed for the Laukžeme wind farm (Lithuania) using time series approach. The ARIMA model was selected and its best structure determined using the historical wind speed data (4 months) and varying both learning interval (3-5 days) of the model and the factual data averaging time (1-6 hours). The accuracy of forecasting was evaluated in terms of RMSE and absolute error. The forecasting results for 39 consecutive time intervals with 6-48 hourly forecasts are presented and discussed.","PeriodicalId":259790,"journal":{"name":"2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":"{\"title\":\"Short-term wind speed forecasting with ARIMA model\",\"authors\":\"V. Radziukynas, A. Klementavicius\",\"doi\":\"10.1109/RTUCON.2014.6998223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper deals with the short-term forecasting of wind speed for the Laukžeme wind farm (Lithuania) using time series approach. The ARIMA model was selected and its best structure determined using the historical wind speed data (4 months) and varying both learning interval (3-5 days) of the model and the factual data averaging time (1-6 hours). The accuracy of forecasting was evaluated in terms of RMSE and absolute error. The forecasting results for 39 consecutive time intervals with 6-48 hourly forecasts are presented and discussed.\",\"PeriodicalId\":259790,\"journal\":{\"name\":\"2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)\",\"volume\":\"180 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"35\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RTUCON.2014.6998223\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTUCON.2014.6998223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short-term wind speed forecasting with ARIMA model
The paper deals with the short-term forecasting of wind speed for the Laukžeme wind farm (Lithuania) using time series approach. The ARIMA model was selected and its best structure determined using the historical wind speed data (4 months) and varying both learning interval (3-5 days) of the model and the factual data averaging time (1-6 hours). The accuracy of forecasting was evaluated in terms of RMSE and absolute error. The forecasting results for 39 consecutive time intervals with 6-48 hourly forecasts are presented and discussed.
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