Adam Neale, Michaël Kummert and Michel Bernier
Article (2020)
Open Access document in PolyPublie |
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Open Access to the full text of this document Accepted Version Terms of Use: All rights reserved Download (517kB) |
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
Subjects: |
2100 Mechanical engineering > 2100 Mechanical engineering 2100 Mechanical engineering > 2107 Modelling, simulation and finite element methods |
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Department: | Department of Mechanical Engineering |
Research Center: | IVADO - Institute for Data Valorization |
Funders: | IVADO |
Grant number: | PRF-2017-12 |
PolyPublie URL: | https://publications.polymtl.ca/5510/ |
Journal Title: | Journal of Building Performance Simulation (vol. 13, no. 5) |
Publisher: | Taylor & Francis |
DOI: | 10.1080/19401493.2020.1800096 |
Official URL: | https://doi.org/10.1080/19401493.2020.1800096 |
Date Deposited: | 14 Dec 2020 10:40 |
Last Modified: | 27 Sep 2024 12:03 |
Cite in APA 7: | 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), 583-605. https://doi.org/10.1080/19401493.2020.1800096 |
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