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Energies 2015, 8(7), 7407-7427; doi:10.3390/en8077407

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

Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland
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Author to whom correspondence should be addressed.
Academic Editor: Thorsten Staake
Received: 16 April 2015 / Revised: 3 June 2015 / Accepted: 6 July 2015 / Published: 22 July 2015
(This article belongs to the Special Issue Smart Metering)
View Full-Text   |   Download PDF [1603 KB, uploaded 22 July 2015]   |  

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. View Full-Text
Keywords: data mining; users’ behaviors; smart metering; smart home; energy usage patterns data mining; users’ behaviors; smart metering; smart home; energy usage patterns
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gajowniczek, K.; Ząbkowski, T. Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data. Energies 2015, 8, 7407-7427.

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