Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2025

Abstract

Electric heating is widespread in Norwegian buildings and significantly contributes to peak loads in the electricity grid. Non-residential buildings are typically heated either by district heating or a combination of electrical heating appliances. Despite its widespread use, most buildings lack sub-meters for electric heating. As a result, the true potential for energy efficiency and load flexibility from heating appliances in buildings remains unknown. Non-intrusive load monitoring and disaggregation techniques offer alternatives to sub-metering by using data-driven methods to extract electricity use for appliances from time-series data. However, little research has been conducted on disaggregating electrical heating loads from low-resolution data, partly due to the scarcity of sub-metered training datasets. Unlike all-electric buildings (EHBs), district heating buildings (DHBs) typically have separate, hourly heating energy meters. This paper examines feature extraction and multiple machine learning algorithms for disaggregation of electricity for heating from AMS-meter data in EHBs, and how cross-domain training from DHBs can contribute to this task. We use sub-metered data from 74 school buildings (54 DHBs and 20 EHBs) in Norway with over 3.8 million hours of recorded measurements, where parts of the dataset are published in a novel public dataset. Results show that CatBoost achieves high performance in disaggregating electricity for heating in EHBs when trained on data from DHBs, with an R2 value of 0.91, NMAE of 2.6%, and a peak load estimation error of 8%, which is an improvement compared to training on EHBs. The study also shows that feature engineering can improve the disaggregation performance in some, but not all EHBs.

Keywords

Energy use, Buildings, Disaggregation, Machine learning, AMS-data, District heating, Transfer learning, Hourly

Discipline

Energy Policy | Engineering

Research Areas

Integrative Research Areas

Publication

Energy and Buildings

Volume

348

First Page

1

Last Page

22

ISSN

0378-7788

Identifier

10.1016/j.enbuild.2025.116359

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.enbuild.2025.116359

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