Application of the RAN algorithm to the problem of short term load forecasting

M. Arahal, E. Camacho
{"title":"Application of the RAN algorithm to the problem of short term load forecasting","authors":"M. Arahal, E. Camacho","doi":"10.23919/ECC.1999.7099627","DOIUrl":null,"url":null,"abstract":"This paper shows the application of the resource allocation network (RAN) algorithm to the problem of electrical load forecasting in a Spanish utility company. The choice of the parameters of the algorithm is usually done manually. In this paper the possibility of automatic selection of parameters is investigated. These parameters are of paramount importance since they determine the final size of the network and its capacity to generalize to new situations. The number of training samples in this kind of problems is usually small. This fact has a strong influence in methods for obtaining neural models, but is rarely taken into account in the forecasting literature. The influence of the available training data is analyzed empirically.","PeriodicalId":117668,"journal":{"name":"1999 European Control Conference (ECC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1999 European Control Conference (ECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ECC.1999.7099627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper shows the application of the resource allocation network (RAN) algorithm to the problem of electrical load forecasting in a Spanish utility company. The choice of the parameters of the algorithm is usually done manually. In this paper the possibility of automatic selection of parameters is investigated. These parameters are of paramount importance since they determine the final size of the network and its capacity to generalize to new situations. The number of training samples in this kind of problems is usually small. This fact has a strong influence in methods for obtaining neural models, but is rarely taken into account in the forecasting literature. The influence of the available training data is analyzed empirically.
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
RAN算法在短期负荷预测问题中的应用
本文介绍了资源分配网络(RAN)算法在西班牙某电力公司电力负荷预测问题中的应用。算法参数的选择通常是手工完成的。本文探讨了参数自动选择的可能性。这些参数至关重要,因为它们决定了网络的最终规模及其适应新情况的能力。这类问题的训练样本数量通常很少。这一事实对获得神经模型的方法有很大的影响,但在预测文献中很少考虑到这一点。对可用训练数据的影响进行了实证分析。
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
A non-conservative approach to set membership identification for control Approximating uncertainty representations using the v-GAP metric Fault-tolerant control of a ship propulsion system using model predictive control Closed-loop identification using canonical correlation analysis Subspace-based identification of MIMO bilinear systems
×
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