Next Article in Journal
Antifungal and Antiaflatoxigenic Activities of 1,8-Cineole and t-Cinnamaldehyde on Aspergillus flavus
Next Article in Special Issue
The State of the Art of the Control Strategies for Single-Phase Electric Springs
Previous Article in Journal
Optical Transmitters without Driver Amplifiers—Optimal Operation Conditions
Open AccessArticle

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

1
Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences SGGW, Warsaw 02-776, Poland
2
Information Systems and Energy Efficient Systems Group, Information Systems and Applied Computer Sciences, University of Bamberg, Bamberg 96047, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(9), 1654; https://doi.org/10.3390/app8091654
Received: 22 August 2018 / Revised: 9 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue Intelligent Energy Management of Electrical Power Systems)
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. View Full-Text
Keywords: smart metering; Grade Correspondence Analysis; machine learning smart metering; Grade Correspondence Analysis; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Gajowniczek, K.; Ząbkowski, T.; Sodenkamp, M. Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms. Appl. Sci. 2018, 8, 1654. https://doi.org/10.3390/app8091654

AMA Style

Gajowniczek K, Ząbkowski T, Sodenkamp M. Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms. Applied Sciences. 2018; 8(9):1654. https://doi.org/10.3390/app8091654

Chicago/Turabian Style

Gajowniczek, Krzysztof; Ząbkowski, Tomasz; Sodenkamp, Mariya. 2018. "Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms" Appl. Sci. 8, no. 9: 1654. https://doi.org/10.3390/app8091654

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop