Short-Term Load Forecasting using Artificial Neural Networks techniques: A case study for Republic of North Macedonia

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
A. Kotevska, N. Rogleva
{"title":"Short-Term Load Forecasting using Artificial Neural Networks techniques: A case study for Republic of North Macedonia","authors":"A. Kotevska, N. Rogleva","doi":"10.59035/mysq1937","DOIUrl":null,"url":null,"abstract":"Modernization and liberalization of power system in North Macedonia offers an opportunity to supervise and regulate the power consumption and power grid. This paper proposes models for short-term load forecasting using artificial neural network in order to balance the demand and load requirements and to determine electricity price. Neural network approach has the advantage of learning directly from the historical data. This method uses multiple data points. Results from the research show that the quality of the short-term prediction depends on the size of the data set and the data transformation.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Information Technologies and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59035/mysq1937","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Modernization and liberalization of power system in North Macedonia offers an opportunity to supervise and regulate the power consumption and power grid. This paper proposes models for short-term load forecasting using artificial neural network in order to balance the demand and load requirements and to determine electricity price. Neural network approach has the advantage of learning directly from the historical data. This method uses multiple data points. Results from the research show that the quality of the short-term prediction depends on the size of the data set and the data transformation.
利用人工神经网络技术进行短期负荷预测:以北马其顿共和国为例
北马其顿电力系统的现代化和自由化为监督和规范电力消费和电网提供了机会。本文提出了利用人工神经网络进行短期负荷预测的模型,以平衡电力需求和负荷需求,确定电价。神经网络方法具有直接从历史数据中学习的优点。该方法使用多个数据点。研究结果表明,短期预测的质量取决于数据集的大小和数据转换。
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 求助全文
来源期刊
自引率
66.70%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信