Big Data Analytics and Predictive Modeling Approaches for the Energy Sector

被引：2
|
作者：
Corizzo, Roberto [1 ]
Ceci, Michelangelo
Malerba, Donato
机构：
[1] Univ Bari Aldo Moro, KDDE Res Grp, Bari, Italy
来源：
2019 IEEE INTERNATIONAL CONGRESS ON BIG DATA (IEEE BIGDATA CONGRESS 2019) | 2019年
关键词：
SELF-ORGANIZING MAP; ANOMALY DETECTION; FORECASTS; SOLAR;
D O I：
10.1109/BigDataCongress.2019.00020
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：55 / 63
页数：9
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