Using explainability tools to inform NILM algorithm performance : a decision tree approach

Mollel, Rachel Stephen and Stankovic, Lina and Stankovic, Vladimir; (2022) Using explainability tools to inform NILM algorithm performance : a decision tree approach. In: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. BuildSys '22 . Association for Computing Machinery (ACM), USA, pp. 368-372. ISBN 9781450398909 (https://doi.org/10.1145/3563357.3566148)

[thumbnail of Mollel-etal-NILM-2022-Using-explainability-tools-to-inform-NILM-algorithm-performance]
Preview
 Text. Filename: Mollel_etal_NILM_2022_Using_explainability_tools_to_inform_NILM_algorithm_performance.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (2MB)| Preview

Abstract

Over the years, Non-Intrusive Load Monitoring (NILM) research has focused on improving performance and more recently, generalizing over distinct datasets. However, the trustworthiness of the NILM model itself has hardly been addressed. To this end, it becomes important to provide a reasoning or explanation behind the predicted outcome for NILM models especially as machine learning models for NILM are often treated as black-box models. With this explanation, the models, not only can be improved, but also build trust for wider adoption within various applications. This paper demonstrates how some explainability tools can be used to explain the outcomes of a decision tree multi-classification approach for NILM and how model explainability results in improved feature selection and eventually performance.

ORCID iDs

Mollel, Rachel Stephen ORCID logoORCID: https://orcid.org/0000-0001-8591-9830, Stankovic, Lina ORCID logoORCID: https://orcid.org/0000-0002-8112-1976 and Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420;
CORE (COnnecting REpositories)