{"title":"利用神经网络进行短期每小时用气量预测","authors":"D. Peharda, M. Delimar, S. Lončarić","doi":"10.1109/ITI.2001.938043","DOIUrl":null,"url":null,"abstract":"This paper presents a neural network based model for forecasting gas consumption for residential and commercial consumers. A feedforward neural network with sigmoid nodes and one hidden layer was trained by backpropagation. The model was validated on real data from a distribution area covering 7% of the total consumption in Croatia, consisting mostly of residential and commercial consumers.","PeriodicalId":375405,"journal":{"name":"Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Short term hourly forecasting of gas consumption using neural networks\",\"authors\":\"D. Peharda, M. Delimar, S. Lončarić\",\"doi\":\"10.1109/ITI.2001.938043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a neural network based model for forecasting gas consumption for residential and commercial consumers. A feedforward neural network with sigmoid nodes and one hidden layer was trained by backpropagation. The model was validated on real data from a distribution area covering 7% of the total consumption in Croatia, consisting mostly of residential and commercial consumers.\",\"PeriodicalId\":375405,\"journal\":{\"name\":\"Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001.\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITI.2001.938043\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITI.2001.938043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short term hourly forecasting of gas consumption using neural networks
This paper presents a neural network based model for forecasting gas consumption for residential and commercial consumers. A feedforward neural network with sigmoid nodes and one hidden layer was trained by backpropagation. The model was validated on real data from a distribution area covering 7% of the total consumption in Croatia, consisting mostly of residential and commercial consumers.
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