NoSQL Data Storage and Clustering Large Volume of Data from Smart Metering Systems with Impact on Electricity Consumption Peak and Tariff Settings
Simona-Vasilica Oprea (),
Adela BÃ¢ra () and
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Simona-Vasilica Oprea: Bucharest University of Economic Studies
Adela BÃ¢ra: Bucharest University of Economic Studies
Dan PreoÈ›escu: Romanian Energy Center
Ovidius University Annals, Economic Sciences Series, 2019, vol. XIX, issue 2, 327-333
Recently, large volumes of electricity consumption data are pouring constantly from smart meters and other sensors that count for millions or even milliards of records. Our purpose in this paper is to handle such data and extract valuable information until it becomes stale. Sometimes, additional data such as meteorological, motion-sensitive, door position data, results from surveys, tariffs, etc. come together with the electricity consumption and increase the number of records. In this case, NoSQL solutions are utilized to process and analyze the entire volume of data. In this paper, we propose a data processing framework for electricity data set that comes from a trial smart metering implementation period that took place from 1st January to 31st December 2010 in Ireland. The main purpose is to cluster the consumers based on similarities regarding theirs 30- minute consumption, show their impact on the electricity consumption peak that could be used as an input in establishing real-time tariffs based on peak coefficient.
Keywords: clustering; big data; NoSQL; electricity consumption; real-time tariff (search for similar items in EconPapers)
JEL-codes: L94 C55 C38 C92 E21 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xix:y:2019:i:2:p:327-333
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