TY - GEN
T1 - Utilizing Interpretable Machine Learning to detect Dynamics in Energy Communities
AU - Zenisek, Jan
AU - Bachinger, Florian
AU - Pitzer, Erik
AU - Wagner, Stefan
AU - Affenzeller, Michael
N1 - Funding Information:
The presented work was done within the project “Secure Prescriptive Analytics”, which is funded by the state of Upper Austria as part of the program "#upperVISION2030".
Publisher Copyright:
© 2022 The Authors.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Data Stream Analysis
KW - Energy Communities
KW - Interpretable Machine Learning
KW - Photovoltaic Systems
KW - Root-Cause Analysis
UR - http://www.scopus.com/inward/record.url?scp=85142881053&partnerID=8YFLogxK
U2 - 10.46354/i3m.2022.emss.042
DO - 10.46354/i3m.2022.emss.042
M3 - Conference contribution
AN - SCOPUS:85142881053
T3 - 34th European Modeling and Simulation Symposium, EMSS 2022
BT - 34th European Modeling and Simulation Symposium, EMSS 2022
A2 - Affenzeller, Michael
A2 - Bruzzone, Agostino G.
A2 - Jimenez, Emilio
A2 - Longo, Francesco
A2 - Petrillo, Antonella
PB - DIME UNIVERSITY OF GENOA
T2 - 34th European Modeling and Simulation Symposium, EMSS 2022
Y2 - 19 September 2022 through 21 September 2022
ER -
