基于神经网络的电力负荷快速预测

M. Lopes, C. R. Minussi, A. Lotufo
{"title":"基于神经网络的电力负荷快速预测","authors":"M. Lopes, C. R. Minussi, A. Lotufo","doi":"10.1109/MWSCAS.2000.952840","DOIUrl":null,"url":null,"abstract":"The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, the backpropagation algorithm with an adaptive process based on fuzzy logic is used. This methodology results in fast training, when compared to the conventional formulation of the backpropagation algorithm. Results are presented using data from a Brazilian electric company and the performance is very good for the proposal objective.","PeriodicalId":437349,"journal":{"name":"Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A fast electric load forecasting using neural networks\",\"authors\":\"M. Lopes, C. R. Minussi, A. Lotufo\",\"doi\":\"10.1109/MWSCAS.2000.952840\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, the backpropagation algorithm with an adaptive process based on fuzzy logic is used. This methodology results in fast training, when compared to the conventional formulation of the backpropagation algorithm. Results are presented using data from a Brazilian electric company and the performance is very good for the proposal objective.\",\"PeriodicalId\":437349,\"journal\":{\"name\":\"Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSCAS.2000.952840\",\"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 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS.2000.952840","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

这项工作的目的是发展一种基于神经网络的电力负荷预测方法。本文采用了基于模糊逻辑的自适应反向传播算法。与传统的反向传播算法相比,这种方法的结果是快速训练。使用巴西电力公司的数据给出了结果,性能非常好,符合提案的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A fast electric load forecasting using neural networks
The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, the backpropagation algorithm with an adaptive process based on fuzzy logic is used. This methodology results in fast training, when compared to the conventional formulation of the backpropagation algorithm. Results are presented using data from a Brazilian electric company and the performance is very good for the proposal objective.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A high speed 3.3V current mode CMOS comparators with 10-b resolution Constraints implementation for IQML and MODE direction-of-arrival estimators A fast electric load forecasting using neural networks Noise reduction in speech signals using a TMS320C31 digital signal processor A high-frequency high-Q CMOS active inductor with DC bias control
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1