COF-Tool: a supervised method and a tool for building classification and disaggregation of electricity for heating from hourly electricity load profiles from buildings

Abstract

Data-driven methods can extract valuable information from building smart meter data. This article presents COF-Tool, a supervised methodological framework and open-source tool with trained models for classifying building categories and heating types, and for disaggregating heating electricity use directly from hourly electric smart meter data of buildings. The tool is trained and validated on real energy use measurements from Norwegian buildings with at least one year of data and consists of two modules: the classification module based on a random forest classifier, trained on data from 6403 buildings across 12 building categories, and the disaggregation module based on a Categorical Boosting (CB) regressor, trained and validated on data from 323 buildings across 9 building categories. To address the scarcity of labelled electric-heating data, the disaggregation module employs a cross-domain training approach where smart electricity and district heating measurements from buildings with district heating (DH) serve as a proxy for all-electric buildings. Results show that the cross-domain approach generalises to school and apartment buildings but is only tested on DH buildings for other categories. The effect of including classification probabilities and extended features in the disaggregation module is also evaluated. Classification performance is reduced compared to the original version, due to an increased number of residential units in the training set and common confusion between apartments and houses, as well as a reduced number of schools in the training set to avoid information leakage, as these buildings are reserved for the disaggregation training set. COF-Tool is publicly available as a GitHub repository and a web application, providing an accessible interface for extracting building information and heating electricity estimates from hourly smart meter data.