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2022 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA

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Data-Driven Machine Learning Model Performance of Real Annual Natural Gas Consumption in Residential Buildings

Matthias Van Hove, Marc Delghust, Jelle Laverge

DOI: https://doi.org/10.26868/25746308.2022.C022
Abstract: To achieve climate neutrality by 2050, Building-Stock Energy Models (BSEMs) are key tools in comparing competing building energy reduction strategies. Yet, at present, existing regulatory energy performance calculation methods poorly estimate the real building energy use and widely overestimates the potential energy savings. Promising data-driven machine learning models, such as gradient boosting machines and support vector machines are gaining considerable traction in a wide range of applications. In this paper, we will evaluate the performance of common data-driven blackbox models and evaluate whether they could potentially replace the present regulatory calculation method for prediction and/or policy making.
Pages: 181 - 188
Paper:
simbuild2022_C022