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The authors would like to thank the Laboratoire des technologies de l'énergie d'Hydro-Québec, the Natural Science and Engineering Research Council of Canada and the Foundation of Université du Québec à Trois-Rivières.

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The building sector is responsible for approximately one-third of the total energy consumption, worldwide. This sector is undergoing a major digital transformation, buildings being more and more equipped with connected devices such as smart meters and IoT devices. This transformation offers the opportunity to better monitor and optimize building operations. In the province of Quebec (Canada), most buildings are equipped with smart meters providing electricity usage data every 15 minutes. A current major challenge is to disaggregate the different energy use from smart meter data, a discipline called non-intrusive load monitoring in literature. In this work, the aim is to develop and validate a potentially generalizable model for all houses that identifies the daily share of each energy use based on building information, weather data and smart meter data. Input features are selected and ordered using an aggregated score composed of the correlation coefficient, the feature importance given by a decision tree, and the predictive power score. Two modelling methods based on quantile regression are tested: linear regression (LR) and gradient boosted decision trees (GBDT). Compared to ordinary least squares regression, quantile methods inherently provide more robustness and confidence intervals. Both models are trained and validated using separate datasets collected in 8 houses in Canada where metering and sub-metering were performed during a whole year. Results on the test dataset indicate a better performance of the GBDT model, compared to the LR model, with a coefficient of determination of 0.88 (vs. 0.78), a mean absolute error of 6.34 % (vs. 8.89 %) and a maximum absolute error between the actual and predicted values in 95 % of the cases of 17.2 % (vs. 23.1 %).