<  Back to the Polytechnique Montréal portal

Discriminant analysis classification of residential electricity smart meter data

Adam Neale, Michaël Kummert, Michel Bernier

Article (2022)

[img] Restricted to: Repository staff only until 10 January 2024
Accepted Version
Terms of Use: Creative Commons Attribution Non-commercial No Derivatives
Request a copy
Show abstract
Hide abstract

Abstract

The objective of this study is to apply machine learning classification to predict building characteristics from electricity smart meter data for the purpose of building stock characterization. Given that there are no publicly available large-scale residential electric smart meter data sets with detailed building characteristics, an open-source virtual smart meter (VSM) data set is used. The VSM data consists of electricity consumption profiles for 200,000 homes with 21 known characteristics, which are used to train predictive models with linear discriminant analysis (LDA). The classification accuracy (CA) is determined for a variety of scenarios where the meter data aggregation and period are varied. The CA depends on the parameter to be classified (the class), the number of data points per building (the features) and the number of buildings used for classification. Reliable classification results are obtained when the number of buildings exceeds the number of features by a significant margin. An application of the developed predictive models to a small data set of 30 real houses illustrates the usefulness of the method but also the challenges in achieving a generalized model with virtual data.

Uncontrolled Keywords

Residential, Smart meter data, Supervised machine learning, Linear discriminant analysis, Building stock characterization, Classification studies

Subjects: 2100 Mechanical engineering > 2100 Mechanical engineering
2100 Mechanical engineering > 2107 Modelling, simulation and finite element methods
2500 Electrical and electronic engineering > 2501 Power systems
2500 Electrical and electronic engineering > 2518 Instrumentation and measurements
Department: Department of Mechanical Engineering
Research Center: CIRDI - Canadian International Resources and Development Institute
Funders: IVADO Fundamental Research Grant
PolyPublie URL: https://publications.polymtl.ca/10339/
Journal Title: Energy & Buildings (vol. 258)
Publisher: Elsevier
DOI: 10.1016/j.enbuild.2021.111823
Official URL: https://doi.org/10.1016/j.enbuild.2021.111823
Date Deposited: 20 May 2022 11:57
Last Modified: 15 Nov 2022 12:45
Cite in APA 7: Neale, A., Kummert, M., & Bernier, M. (2022). Discriminant analysis classification of residential electricity smart meter data. Energy & Buildings, 258, 111823 (18 pages). https://doi.org/10.1016/j.enbuild.2021.111823

Statistics

Total downloads

Downloads per month in the last year

Origin of downloads

Dimensions

Repository Staff Only

View Item View Item