Abstract

With the growing use of machine learning models in many critical domains, research regarding making these models, as well as their predictions, more explainable has intensified in the last few years. In this paper, we present extensions to the machine learning based data mining technique Variable Interaction Networks (VIN), to integrate existing domain knowledge and thus, enable more meaningful analysis. Several tests on data from a case study concerned with long-term monitored photovoltaic systems, verify the feasibility of our approach to provide valuable, human-interpretable insights. In particular, we show the successful application of root-cause detection in scenarios with changing system conditions.

OriginalspracheEnglisch
Titel34th European Modeling and Simulation Symposium, EMSS 2022
Redakteure/-innenMichael Affenzeller, Agostino G. Bruzzone, Emilio Jimenez, Francesco Longo, Antonella Petrillo
Herausgeber (Verlag)DIME UNIVERSITY OF GENOA
ISBN (elektronisch)9788885741737
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung34th European Modeling and Simulation Symposium, EMSS 2022 - Rome, Italien
Dauer: 19 Sep. 202221 Sep. 2022

Publikationsreihe

Name34th European Modeling and Simulation Symposium, EMSS 2022

Konferenz

Konferenz34th European Modeling and Simulation Symposium, EMSS 2022
Land/GebietItalien
OrtRome
Zeitraum19.09.202221.09.2022

Fingerprint

Untersuchen Sie die Forschungsthemen von „Utilizing Interpretable Machine Learning to detect Dynamics in Energy Communities“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren