Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms





Faculty/Professorship: Information Systems and Energy Efficient Systems  
Author(s): Gajowniczek, Krzysztof; Ząbkowski, Tomasz; Sodenkamp, Mariya
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2021
Pages: 1-24
Source/Other editions: Applied Sciences : open access journal, 8 (2018), 9, S. 1-24 - ISSN: 2076-3417
is version of: 10.3390/app8091654
Year of first publication: 2018
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
DOI: 10.3390/app8091654
URN: urn:nbn:de:bvb:473-irb-497724
Abstract: 
In this article, the Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to extract important features to reveal households’ characteristics based on electricity usage data. The main goal of the analysis is to automatically extract, in a non-intrusive way, number of socio-economic household properties including family type, age of inhabitants, employment type, house type, and number of bedrooms. The knowledge of specific properties enables energy utilities to develop targeted energy conservation tariffs and to assure balanced operation management. In particular, classification of the households based on the electricity usage delivers value added information to allow accurate demand planning with the goal to enhance the overall efficiency of the network. The approach was evaluated by analyzing smart meter data collected from 4182 households in Ireland over a period of 1.5 years. The analysis outcome shows that revealing characteristics from smart meter data is feasible, and the proposed machine learning methods were yielding for an accuracy of approx. 90% and Area Under Receiver Operating Curve (AUC) of 0.82.
Keywords: smart metering; Grade Correspondence Analysis; machine learning
Peer Reviewed: Ja
International Distribution: Ja
Open Access Journal: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/49772
Release Date: 28. April 2021

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