{"title":"基于改进遗传算法和BP神经网络的天然气负荷预测","authors":"Yihan Tang","doi":"10.1109/ICKII55100.2022.9983572","DOIUrl":null,"url":null,"abstract":"The prediction accuracy of the traditional method is low in the natural gas load prediction. Thus, to improve the prediction accuracy of the natural gas load, a new improved scheme is came up with. A natural gas load predicting way is based on improved genetic algorithm and BP neural network. Compared with the traditional BP prediction algorithm and GA-BP algorithm, the error optimization performance by the proposed method is better, with an average error of 3.22%, which has a certain engineering application value.","PeriodicalId":352222,"journal":{"name":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Natural Gas Load Forecasting Based on Improved Genetic Algorithm and BP Neural Network\",\"authors\":\"Yihan Tang\",\"doi\":\"10.1109/ICKII55100.2022.9983572\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The prediction accuracy of the traditional method is low in the natural gas load prediction. Thus, to improve the prediction accuracy of the natural gas load, a new improved scheme is came up with. A natural gas load predicting way is based on improved genetic algorithm and BP neural network. Compared with the traditional BP prediction algorithm and GA-BP algorithm, the error optimization performance by the proposed method is better, with an average error of 3.22%, which has a certain engineering application value.\",\"PeriodicalId\":352222,\"journal\":{\"name\":\"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICKII55100.2022.9983572\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICKII55100.2022.9983572","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Natural Gas Load Forecasting Based on Improved Genetic Algorithm and BP Neural Network
The prediction accuracy of the traditional method is low in the natural gas load prediction. Thus, to improve the prediction accuracy of the natural gas load, a new improved scheme is came up with. A natural gas load predicting way is based on improved genetic algorithm and BP neural network. Compared with the traditional BP prediction algorithm and GA-BP algorithm, the error optimization performance by the proposed method is better, with an average error of 3.22%, which has a certain engineering application value.