Studies of short-term load forecasting model based on LSTM-NBEATS

Song Huang, Danhong Zhang, Tuo Zheng, Guangbo Tong, Jianxin Xu, Fangzheng Jia
{"title":"Studies of short-term load forecasting model based on LSTM-NBEATS","authors":"Song Huang, Danhong Zhang, Tuo Zheng, Guangbo Tong, Jianxin Xu, Fangzheng Jia","doi":"10.1109/CAC57257.2022.10055018","DOIUrl":null,"url":null,"abstract":"Due to the current rising in oil prices and energy scarcity, the role of short term load forecasting is critical in basic functioning and scheduling of power systems. The forecasting accuracy of a single model always has its limitations. Therefore, the LSTM-NBEATS model, a combined model combining LSTM and NBEATS by a MAPE (mean absolute percentage error) weighting method is proposed. This model is easy to realize and train, and does not rely on complicated feature engineering. It is applied to hourly load datasets from three European countries, Macedonia (MK), Latvia (LV), and Poland (PL). In this paper, experimental results show that in short term load forecasting the model proposed performs effective.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 China Automation Congress (CAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAC57257.2022.10055018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Due to the current rising in oil prices and energy scarcity, the role of short term load forecasting is critical in basic functioning and scheduling of power systems. The forecasting accuracy of a single model always has its limitations. Therefore, the LSTM-NBEATS model, a combined model combining LSTM and NBEATS by a MAPE (mean absolute percentage error) weighting method is proposed. This model is easy to realize and train, and does not rely on complicated feature engineering. It is applied to hourly load datasets from three European countries, Macedonia (MK), Latvia (LV), and Poland (PL). In this paper, experimental results show that in short term load forecasting the model proposed performs effective.
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LSTM-NBEATS的短期负荷预测模型研究
由于当前石油价格的上涨和能源的短缺,短期负荷预测在电力系统的基本运行和调度中起着至关重要的作用。单一模型的预测精度总是有其局限性的。为此,提出了LSTM-NBEATS模型,即通过MAPE (mean absolute percentage error)加权法将LSTM和NBEATS结合起来的组合模型。该模型易于实现和训练,不依赖于复杂的特征工程。它应用于三个欧洲国家的每小时负荷数据集,马其顿(MK),拉脱维亚(LV)和波兰(PL)。实验结果表明,该模型在短期负荷预测中是有效的。
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
Single Object Tracking in Satellite Videos with Meta-updater and Knowledge Distillation An improved event-trigger-based robust 6-DOF spacecraft formation control scheme under restricted communication Adaptive Neural Fixed-time Tracking Control of Underactuated USVs With External Disturbances Computer-Aided Diagnosis of COVID-19 with Joint Instance Segmentation and Classification Prescribed-Time Backstepping Algorithms for Leader-Follower Multi-Agent 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