Photovoltaic power plant production operational forecast based on its short-term forecasting model

A. Khalyasmaa, S. Eroshenko, Duc Chung Tran, Snegirev Denis
{"title":"Photovoltaic power plant production operational forecast based on its short-term forecasting model","authors":"A. Khalyasmaa, S. Eroshenko, Duc Chung Tran, Snegirev Denis","doi":"10.1109/ICSTCEE49637.2020.9276846","DOIUrl":null,"url":null,"abstract":"This paper addresses the study of operational photovoltaic power plant forecasting based on the results of short-term forecasts, thus providing the multi-level hierarchical system of solar power plant generation planning. The study provides the comparison between naive persistence, autoregressive and autoregressive moving average models with the corresponding parameters tuning in order to identify the most effective way to implement intra-day forecasting option. The case study is based on real photovoltaic power plant operational data in order to verify the opportunity of the presented approach practical implementation.","PeriodicalId":113845,"journal":{"name":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","volume":"325 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSTCEE49637.2020.9276846","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 addresses the study of operational photovoltaic power plant forecasting based on the results of short-term forecasts, thus providing the multi-level hierarchical system of solar power plant generation planning. The study provides the comparison between naive persistence, autoregressive and autoregressive moving average models with the corresponding parameters tuning in order to identify the most effective way to implement intra-day forecasting option. The case study is based on real photovoltaic power plant operational data in order to verify the opportunity of the presented approach practical implementation.
基于其短期预测模型的光伏电站生产运行预测
本文研究了基于短期预测结果的运行光伏电站预测,从而提供了太阳能电站发电规划的多层次分层体系。本研究提供了朴素持续、自回归和自回归移动平均模型的比较,并进行了相应的参数调整,以确定实现日内预测选项的最有效方法。案例研究是基于真实的光伏电站运行数据,以验证所提出的方法的实际实施的机会。
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
0.00%
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