<  Back to the Polytechnique Montréal portal

Development of a stochastic virtual smart meter data set for a residential building stock - methodology and sample data

Adam Neale, Michaël Kummert and Michel Bernier

Article (2020)

Accepted Version
Terms of Use: All rights reserved.
Download (580kB)
Cite this document: Neale, A., Kummert, M. & Bernier, M. (2020). Development of a stochastic virtual smart meter data set for a residential building stock - methodology and sample data. Journal of Building Performance Simulation, 13(5), p. 583-605. doi:10.1080/19401493.2020.1800096
Show abstract Hide abstract


Existing electricity smart meter data sets lack sufficient details on building parameters to evaluate the impact that home characteristics can have on electricity consumption. An extensive, open-source virtual smart meter (VSM) data set with corresponding building characteristics is provided. The methodology used to develop the VSM data is presented in detail. The data set consists of a variety of homes representative of a subset of the Canadian single-family home building stock. The building characteristics cover a wide range of values that are based on probability distributions developed using a segmentation and characterization process. The resulting framework and VSM data set can be used by researchers to develop classification models, verify load disaggregation algorithms, and for a variety of other purposes.

Uncontrolled Keywords

smart meter data; building energy simulation; residential

Open Access document in PolyPublie
Subjects: 2100 Génie mécanique > 2100 Génie mécanique
2100 Génie mécanique > 2107 Modélisation, simulation et méthodes des éléments finis
Department: Département de génie mécanique
Research Center: IVADO - Institut de valorisation des données
Funders: IVADO
Grant number: PRF-2017-12
Date Deposited: 14 Dec 2020 10:40
Last Modified: 01 Sep 2021 01:15
PolyPublie URL: https://publications.polymtl.ca/5510/
Document issued by the official publisher
Journal Title: Journal of Building Performance Simulation (vol. 13, no. 5)
Publisher: Taylor & Francis
Official URL: https://doi.org/10.1080/19401493.2020.1800096


Total downloads

Downloads per month in the last year

Origin of downloads


Repository Staff Only