Data-driven building energy modelling – An analysis of the potential for generalisation through interpretable machine learning

Massimiliano Manfren, Patrick AB. James and Lamberto Tronchin

Renewable and Sustainable Energy Reviews, 2022, vol. 167, issue C

Abstract: Data-driven building energy modelling techniques have proven to be effective in multiple applications. However, the debate around the possibility of generalisation is open. Generalisation involves the ability of a machine-learning model to adapt to previously unseen data and perform in a satisfactory way. Besides that, while machine-learning techniques are extremely powerful, interpretability, i.e. the ability for humans to predict how the model output will change in response to a change in input data or algorithmic parameters, is essential to attain a “human-in-the-loop” approach and creating feedback loops aimed at continuous improvement of efficiency measures in buildings.

Keywords: Data-driven energy modelling; Interpretable machine-learning; Regression-based approaches; Generalisation; Building energy modelling; Measurement and verification; Energy analytics (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032122005779
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:167:y:2022:i:c:s1364032122005779

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2022.112686

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().