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Data-Driven Building Energy Modelling – Generalisation Potential of Energy Signatures Through Interpretable Machine Learning

TitoloData-Driven Building Energy Modelling – Generalisation Potential of Energy Signatures Through Interpretable Machine Learning
Tipo di pubblicazionePresentazione a Congresso
Anno di Pubblicazione2022
AutoriManfren, Massimiliano, Tommasino Maria Cristina, and Tronchin Lamberto
Conference NameBuilding Simulation Applications
EditoreFree University of Bozen Bolzano
Abstract

Building energy modeling based on data-driven techniques has been demonstrated to be effective in a variety of situations. However, the question about its limits in terms of generalization is still open. The ability of a machine-learning model to adapt to previously unseen data and function satisfactorily is known as generalization. Apart from that, while machine-learning techniques are incredibly effective, interpretability is required for a "human-in-the-loop" approach to be successful. This study develops and tests a flexible regression-based approach applied to monitored energy data on a Passive House building. The formulation employs dummy (binary) variables as a piecewise linearization method, with the procedures for producing them explicitly stated to ensure interpretability. The results are described using statistical indicators and a graphic technique that allows for comparison across levels in the building systems. Finally, suggestions are provided for further steps toward generalization in data-driven techniques for energy in buildings. © 2022 Free University of Bozen Bolzano. All rights reserved.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176499347&partnerID=40&md5=6be9683dacded7bff3e1fd9a60ccc6c5
Citation KeyManfren2022255