Data pre-processing for short-term load forecasting in an autonomous power system using artificial neural networks

S. Kiartzis, C. Zoumas, A. Bakirtzis, V. Petridis
{"title":"Data pre-processing for short-term load forecasting in an autonomous power system using artificial neural networks","authors":"S. Kiartzis, C. Zoumas, A. Bakirtzis, V. Petridis","doi":"10.1109/ICECS.1996.584560","DOIUrl":null,"url":null,"abstract":"This paper presents the development of an Artificial Neural Network (ANN) based short-term load forecasting model for the Dispatching Center of the Greek Public Power Corporation (PPC) in the island of Crete. The model can forecast daily load profiles with a lead time of one to seven days. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set pre-processing are described in the paper.","PeriodicalId":402369,"journal":{"name":"Proceedings of Third International Conference on Electronics, Circuits, and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Third International Conference on Electronics, Circuits, and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECS.1996.584560","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

Abstract

This paper presents the development of an Artificial Neural Network (ANN) based short-term load forecasting model for the Dispatching Center of the Greek Public Power Corporation (PPC) in the island of Crete. The model can forecast daily load profiles with a lead time of one to seven days. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set pre-processing are described in the paper.
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
基于人工神经网络的自主电力系统短期负荷预测数据预处理
针对希腊公共电力公司(PPC)位于克里特岛的调度中心,建立了基于人工神经网络的短期负荷预测模型。该模型可以预测每日的负荷概况,提前时间为1至7天。本文介绍了模型开发过程中在输入变量的选择、人工神经网络结构和训练数据集预处理等方面所取得的经验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Low-power digital PLL with one cycle frequency lock-in time and large frequency-multiplication factor for advanced power management Application of direct iteration in harmonic balance analysis of LC oscillators A Hilbert fractal codec for region oriented compression of color images Wideband CMOS analog cells for video and wireless communications Programmable sampled data filter with low sensitivity implementation
×
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