University of Oulu

F. Villar, L. C. Pereira da Silva, P. Henrique Juliano Nardelli and H. Hazini, "Detection of Appliance Utilization Patterns via Dimensionality Reduction," 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America), Gramado, Brazil, 2019, pp. 1-6, doi: 10.1109/ISGT-LA.2019.8895285

Detection of appliance utilization patterns via dimensionality reduction

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Author: Villar, Fernanda1; da Silva, Luiz Carlos Pereira1; Nardelli, Pedro Henrique Juliano2,3;
Organizations: 1School of Electrical and Computer Engineering, State University of Campinas, Brazil
2School of Energy Systems, LUT University, Finland
3Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-06-26


This paper focuses on the detection of utilization patterns in electricity residential consumption, which are closely related to the occupant characteristics (e.g. number, age, occupancy, and social class). Our goal is to identify groups of appliances that are often used together via their statistically relatedness. This relation might be obvious (as in TV and Home Theater), or not. The results can be used, for example, to guide a recommendations letter from the energy supplier to the final user, suggesting specific change of habits in order to improve the residence’s energy efficiency. We propose here a methodology for identifying patterns from a large sets of system status, which is a computationally hard task defined in ℝ n with n being the number of appliances involved. The approach consist in the following steps: (i) the Principal Component Analysis method is employed to reduce the set dimensionality to ℝ 3 with explained variance from 68% to 90% to guarantee minimum information loses, (ii) the k-means method to clustering appliances into different groups and (iii) the elbow method was used to define the best number of clusters for each house with explained variance of at least 93% and reaching more than 99% for the best k. Numerical tests using the UK-DALE dataset are presented to show the effectiveness of the proposed solution. The main contribution of this work is a method with low computational cost that requires no other information than a large set of reliable system status (binary vectors) to reveal utilization patterns in the residence.

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ISBN: 978-1-5386-9567-8
ISBN Print: 978-1-5386-9568-5
Pages: 1 - 6
Article number: 8895285
DOI: 10.1109/ISGT-LA.2019.8895285
Host publication: 2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019, 15-18 September, Gramado, Brazil
Conference: IEEE PES Europe Conference on Innovative Smart Grid Technologies
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is partly funded by Academy of Finland via ee- IoT (ICT2023/n.319009), BCDC Energy (SRC/n.292854), and 6Genesis Flagship (n. 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
292854 (Academy of Finland Funding decision)
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