Day ahead load forecasting models for holidays in Indian context

R. S. Fernandes, Y. Bichpuriya, M. Rao, S. Soman
{"title":"Day ahead load forecasting models for holidays in Indian context","authors":"R. S. Fernandes, Y. Bichpuriya, M. Rao, S. Soman","doi":"10.1109/ICPES.2011.6156652","DOIUrl":null,"url":null,"abstract":"Accurate Short Term Load Forecasting (STLF) is critical for efficient functioning of electricity distribution company. High forecast error may result in non-optimal system operations and financial risk in short term power markets. Load profiles on holidays are very different from that on normal days. In India, holidays can be categorized as Sunday, Public holidays (e.g., Independence day, Republic day etc.) and Festival days (e.g., Diwali, Eid, Christmas etc.). Apart from these holidays, there are a few regional holidays e.g., Ganesh Chaturthi and Maharashtra day in the state of Maharashtra. Sunday is a repeated holiday having weekly frequency while other holidays come once in a year. Also, some of these holidays follows lunar calender and some follows Gregorian calender. Each holiday, excluding Sunday and Public holidays, has different characteristics in terms of activities, lighting load and the number of peoples celebrating the holiday. In such a scenario, predicting the accurate load profile for the holidays is a difficult task. This paper proposes two different models for Sunday and other holidays. Sunday model is used for forecasting load profile on Sundays and Holiday model is used for all other public holidays and festival days. The proposed models have been tested on load data of an urban distribution utilities and the results are illustrated.","PeriodicalId":158903,"journal":{"name":"2011 International Conference on Power and Energy Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Power and Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPES.2011.6156652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Accurate Short Term Load Forecasting (STLF) is critical for efficient functioning of electricity distribution company. High forecast error may result in non-optimal system operations and financial risk in short term power markets. Load profiles on holidays are very different from that on normal days. In India, holidays can be categorized as Sunday, Public holidays (e.g., Independence day, Republic day etc.) and Festival days (e.g., Diwali, Eid, Christmas etc.). Apart from these holidays, there are a few regional holidays e.g., Ganesh Chaturthi and Maharashtra day in the state of Maharashtra. Sunday is a repeated holiday having weekly frequency while other holidays come once in a year. Also, some of these holidays follows lunar calender and some follows Gregorian calender. Each holiday, excluding Sunday and Public holidays, has different characteristics in terms of activities, lighting load and the number of peoples celebrating the holiday. In such a scenario, predicting the accurate load profile for the holidays is a difficult task. This paper proposes two different models for Sunday and other holidays. Sunday model is used for forecasting load profile on Sundays and Holiday model is used for all other public holidays and festival days. The proposed models have been tested on load data of an urban distribution utilities and the results are illustrated.
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
印度假期日前负荷预测模型
准确的短期负荷预测是配电公司高效运行的关键。在短期电力市场中,较高的预测误差可能导致系统非最优运行和财务风险。节假日的负荷分布与平时有很大的不同。在印度,假期可以分为周日、公共假期(如独立日、共和国日等)和节日(如排灯节、开斋节、圣诞节等)。除了这些节日,还有一些地区性的节日,如马哈拉施特拉邦的Ganesh Chaturthi和Maharashtra日。星期天是一个重复的节日,每周都有频率,而其他节日一年只有一次。此外,有些节日遵循农历,有些遵循公历。每个节日,除了星期日和公众假期,在活动、照明负荷和庆祝节日的人数方面都有不同的特点。在这种情况下,预测假日的准确负载概况是一项艰巨的任务。本文针对周日和其他节假日提出了两种不同的模型。周日模型用于预测周日的负荷分布,假日模型用于预测所有其他公众假期和节日。通过对某城市配电网的负荷数据进行了验证,并给出了验证结果。
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
On the optimal tuning of FACTS based stabilizers for dynamic stability enhancement in multimachine power systems A new proposal for voltage regulation multi feeders/Multibus systems using MC-DVR Deployment of System Protection Schemes for enhancing reliability of power system: Operational experience of wide area SPS in Northern Regional Power System in India Power quality improvement in DTC based induction motor drive using Minnesota rectifier Neural learning algorithm based power quality enhancement for three phase three wire distribution system utilizing shunt active power filter strategy
×
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