能源领域的大数据分析和预测建模方法

Roberto Corizzo, Michelangelo Ceci, D. Malerba
{"title":"能源领域的大数据分析和预测建模方法","authors":"Roberto Corizzo, Michelangelo Ceci, D. Malerba","doi":"10.1109/BigDataCongress.2019.00020","DOIUrl":null,"url":null,"abstract":"This paper describes recent results achieved in the analysis of geo-distributed sensor data generated in the context of the energy sector. The approaches described have roots in the Big Data Analytics and Predictive Modeling research fields and are based on distributed architectures. They tackle the energy forecasting task for a network of energy production plants, by also taking into consideration the detection and treatment of anomalies in the data. This research is motivated by and consistent with the objectives of research projects funded by the European Commission and by many national governments.","PeriodicalId":335850,"journal":{"name":"2019 IEEE International Congress on Big Data (BigDataCongress)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Big Data Analytics and Predictive Modeling Approaches for the Energy Sector\",\"authors\":\"Roberto Corizzo, Michelangelo Ceci, D. Malerba\",\"doi\":\"10.1109/BigDataCongress.2019.00020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes recent results achieved in the analysis of geo-distributed sensor data generated in the context of the energy sector. The approaches described have roots in the Big Data Analytics and Predictive Modeling research fields and are based on distributed architectures. They tackle the energy forecasting task for a network of energy production plants, by also taking into consideration the detection and treatment of anomalies in the data. This research is motivated by and consistent with the objectives of research projects funded by the European Commission and by many national governments.\",\"PeriodicalId\":335850,\"journal\":{\"name\":\"2019 IEEE International Congress on Big Data (BigDataCongress)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Congress on Big Data (BigDataCongress)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BigDataCongress.2019.00020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Congress on Big Data (BigDataCongress)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigDataCongress.2019.00020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

本文描述了在能源部门背景下产生的地理分布式传感器数据分析中取得的最新成果。所描述的方法植根于大数据分析和预测建模研究领域,并且基于分布式架构。他们通过考虑数据异常的检测和处理,来解决能源生产工厂网络的能源预测任务。这项研究是由欧洲委员会和许多国家政府资助的研究项目的目标所推动和一致的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Big Data Analytics and Predictive Modeling Approaches for the Energy Sector
This paper describes recent results achieved in the analysis of geo-distributed sensor data generated in the context of the energy sector. The approaches described have roots in the Big Data Analytics and Predictive Modeling research fields and are based on distributed architectures. They tackle the energy forecasting task for a network of energy production plants, by also taking into consideration the detection and treatment of anomalies in the data. This research is motivated by and consistent with the objectives of research projects funded by the European Commission and by many national governments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
PREMISES, a Scalable Data-Driven Service to Predict Alarms in Slowly-Degrading Multi-Cycle Industrial Processes Context-Aware Enforcement of Privacy Policies in Edge Computing Efficient Re-Computation of Big Data Analytics Processes in the Presence of Changes: Computational Framework, Reference Architecture, and Applications Reducing Feature Embedding Data for Discovering Relations in Big Text Data Distributed, Numerically Stable Distance and Covariance Computation with MPI for Extremely Large Datasets
×
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