{"title":"Coal Requirement Prediction Using BP Neural Network","authors":"Xu Xin, Xuli Hong","doi":"10.1109/ICEE.2010.956","DOIUrl":null,"url":null,"abstract":"Coal is one of the most important main energy-consuming resources in our society. It is important to forecast the coal requirement with high accuracy. BP neural network forecasting model has the typical of self-learning and self-adaptation. It is often used in these systems that are difficult to create accurate mathematical model. The factors such as the trend of the industrial coal, the rate of increased GDP, rice index and the proportion in the energy-consuming of coal are considered in this paper. We use improved BP model to predict and simulate in MATLAB. It proves that this prediction has better application.","PeriodicalId":420284,"journal":{"name":"2010 International Conference on E-Business and E-Government","volume":"130 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on E-Business and E-Government","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEE.2010.956","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Coal is one of the most important main energy-consuming resources in our society. It is important to forecast the coal requirement with high accuracy. BP neural network forecasting model has the typical of self-learning and self-adaptation. It is often used in these systems that are difficult to create accurate mathematical model. The factors such as the trend of the industrial coal, the rate of increased GDP, rice index and the proportion in the energy-consuming of coal are considered in this paper. We use improved BP model to predict and simulate in MATLAB. It proves that this prediction has better application.