Data Mining Smart Energy Time Series

Janina Popeanga ()
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Janina Popeanga: University of Economic Studies, Bucharest, Romania

Database Systems Journal, 2015, vol. 6, issue 1, 14-22

Abstract: With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

Keywords: Time Series Data Mining; Clustering; Classification; Motif Discovery; Data Reduction (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:6:y:2015:i:1:p:14-22

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