{"title":"基于改进EBP神经网络的回水效应水电站流量预测研究","authors":"Changyu Liu, Wei Liu","doi":"10.1109/PES.2003.1267367","DOIUrl":null,"url":null,"abstract":"In optimal dispatching decision system of hydropower station, discharge of reservoir is forecast usually. However the discharge has complicated nonlinear relations with downriver level and backwater flux when backwater effects exist. It is difficult to get satisfactory forecasting results with traditional linear interpolation method. This paper proposes a nonlinear decision-making method based on error back propagation (EBP) artificial neural network (ANN) to establish forecasting discharge model of reservoir. Improved EBP algorithm is presented to process ANN model training. Simulation results show that the proposed method forecasts discharge with the backwater effect better than traditional linear interpolation method. The ANN model and improved EBP algorithm proposed are also applicable to other similar system.","PeriodicalId":131986,"journal":{"name":"2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Study on forecasting discharge of hydropower station with backwater effect based on improved EBP neural network\",\"authors\":\"Changyu Liu, Wei Liu\",\"doi\":\"10.1109/PES.2003.1267367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In optimal dispatching decision system of hydropower station, discharge of reservoir is forecast usually. However the discharge has complicated nonlinear relations with downriver level and backwater flux when backwater effects exist. It is difficult to get satisfactory forecasting results with traditional linear interpolation method. This paper proposes a nonlinear decision-making method based on error back propagation (EBP) artificial neural network (ANN) to establish forecasting discharge model of reservoir. Improved EBP algorithm is presented to process ANN model training. Simulation results show that the proposed method forecasts discharge with the backwater effect better than traditional linear interpolation method. The ANN model and improved EBP algorithm proposed are also applicable to other similar system.\",\"PeriodicalId\":131986,\"journal\":{\"name\":\"2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PES.2003.1267367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PES.2003.1267367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on forecasting discharge of hydropower station with backwater effect based on improved EBP neural network
In optimal dispatching decision system of hydropower station, discharge of reservoir is forecast usually. However the discharge has complicated nonlinear relations with downriver level and backwater flux when backwater effects exist. It is difficult to get satisfactory forecasting results with traditional linear interpolation method. This paper proposes a nonlinear decision-making method based on error back propagation (EBP) artificial neural network (ANN) to establish forecasting discharge model of reservoir. Improved EBP algorithm is presented to process ANN model training. Simulation results show that the proposed method forecasts discharge with the backwater effect better than traditional linear interpolation method. The ANN model and improved EBP algorithm proposed are also applicable to other similar system.