Next Article in Journal
Facile and Economical Preparation of SiAlON-Based Composites Using Coal Gangue: From Fundamental to Industrial Application
Next Article in Special Issue
An Optimal PR Control Strategy with Load Current Observer for a Three-Phase Voltage Source Inverter
Previous Article in Journal
Automated Linear Function Submission-Based Double Auction as Bottom-up Real-Time Pricing in a Regional Prosumers’ Electricity Network
Previous Article in Special Issue
Smart Home Communication Technologies and Applications: Wireless Protocol Assessment for Home Area Network Resources
Open AccessArticle

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
*
Author to whom correspondence should be addressed.
Academic Editor: Thorsten Staake
Energies 2015, 8(7), 7407-7427; https://doi.org/10.3390/en8077407
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)
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
Show Figures

Figure 1

MDPI and ACS Style

Gajowniczek, K.; Ząbkowski, T. Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data. Energies 2015, 8, 7407-7427. https://doi.org/10.3390/en8077407

AMA Style

Gajowniczek K, Ząbkowski T. Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data. Energies. 2015; 8(7):7407-7427. https://doi.org/10.3390/en8077407

Chicago/Turabian Style

Gajowniczek, Krzysztof; Ząbkowski, Tomasz. 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data" Energies 8, no. 7: 7407-7427. https://doi.org/10.3390/en8077407

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Search more from Scilit
 
Search
Back to TopTop