Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data

Krzysztof Gajowniczek and Tomasz Ząbkowski
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Krzysztof Gajowniczek: Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland
Tomasz Ząbkowski: Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland

Energies, 2015, vol. 8, issue 7, 1-21

Abstract: The main goal of this research is to discover the structure of home appliances usage patterns, hence providing more intelligence in smart metering systems by taking into account the usage of selected home appliances and the time of their usage. In particular, we present and apply a set of unsupervised machine learning techniques to reveal specific usage patterns observed at an individual household. The work delivers the solutions applicable in smart metering systems that might: (1) contribute to higher energy awareness; (2) support accurate usage forecasting; and (3) provide the input for demand response systems in homes with timely energy saving recommendations for users. The results provided in this paper show that determining household characteristics from smart meter data is feasible and allows for quickly grasping general trends in data.

Keywords: data mining; users’ behaviors; smart metering; smart home; energy usage patterns (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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