Explainable cyber-physical energy systems based on knowledge graph

Peb Ruswono Aryan; Fajar J. Ekaputra; Reka Marta Sabou; Daniel Hauer; Ralf Mosshammer; Alfred Einfalt; Tomasz Miksa; Andreas Rauber

doi:10.1145/3470481.3472704

Explainable cyber-physical energy systems based on knowledge graph

Peb Ruswono Aryan, Fajar J. Ekaputra, Reka Marta Sabou, Daniel Hauer, Ralf Mosshammer, Alfred Einfalt, Tomasz Miksa, Andreas Rauber

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

Abstract

Explainability can help cyber-physical systems alleviating risk in automating decisions that are affecting our life. Building an explainable cyber-physical system requires deriving explanations from system events and causality between the system elements. Cyber-physical energy systems such as smart grids involve cyber and physical aspects of energy systems and other elements, namely social and economic. Moreover, a smart-grid scale can range from a small village to a large region across countries. Therefore, integrating these varieties of data and knowledge is a fundamental challenge to build an explainable cyber-physical energy system. This paper aims to use knowledge graph based framework to solve this challenge. The framework consists of an ontology to model and link data from various sources and graph-based algorithm to derive explanations from the events. A simulated demand response scenario covering the above aspects further demonstrates the applicability of this framework.

Original language	English
Title of host publication	MSCPES'21 : Proceedings of the 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems
Editors	Peter Palensky, Anurag Srivastava
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	1 - 6
ISBN (Electronic)	9781450386081
DOIs	https://doi.org/10.1145/3470481.3472704
Publication status	Published - 19 May 2021
Externally published	Yes
Event	9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, MSCPES 2021, Held as part of the Cyber-Physical Systems and Internet-of-Things Week 2021 - Virtual, Online, United States Duration: 18 May 2021 → …

Conference

Conference	9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, MSCPES 2021, Held as part of the Cyber-Physical Systems and Internet-of-Things Week 2021
Country/Territory	United States
City	Virtual, Online
Period	18/05/21 → …

Bibliographical note

Funding Information:
This work was funded by the Austrian Research Promotion Agency (FFG) in the PoSyCo project (FFG No. 3036508).

Publisher Copyright:
© 2021 ACM.

Austrian Classification of Fields of Science and Technology (ÖFOS)

102
102001 Artificial intelligence
102015 Information systems
102022 Software development

Keywords

Explainability
Knowledge Graphs
Ontologies
Smart Grid Simulation
Smart grids

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Aryan, P. R., Ekaputra, F. J., Sabou, R. M., Hauer, D., Mosshammer, R., Einfalt, A., Miksa, T., & Rauber, A. (2021). Explainable cyber-physical energy systems based on knowledge graph. In Peter Palensky, Anurag Srivastava (Ed.), MSCPES'21 : Proceedings of the 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (pp. 1 - 6). [4] Association for Computing Machinery. https://doi.org/10.1145/3470481.3472704

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abstract = "Explainability can help cyber-physical systems alleviating risk in automating decisions that are affecting our life. Building an explainable cyber-physical system requires deriving explanations from system events and causality between the system elements. Cyber-physical energy systems such as smart grids involve cyber and physical aspects of energy systems and other elements, namely social and economic. Moreover, a smart-grid scale can range from a small village to a large region across countries. Therefore, integrating these varieties of data and knowledge is a fundamental challenge to build an explainable cyber-physical energy system. This paper aims to use knowledge graph based framework to solve this challenge. The framework consists of an ontology to model and link data from various sources and graph-based algorithm to derive explanations from the events. A simulated demand response scenario covering the above aspects further demonstrates the applicability of this framework.",

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note = "Funding Information: This work was funded by the Austrian Research Promotion Agency (FFG) in the PoSyCo project (FFG No. 3036508). Publisher Copyright: {\textcopyright} 2021 ACM.; 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, MSCPES 2021, Held as part of the Cyber-Physical Systems and Internet-of-Things Week 2021 ; Conference date: 18-05-2021",

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Aryan, PR, Ekaputra, FJ , Sabou, RM, Hauer, D, Mosshammer, R, Einfalt, A, Miksa, T & Rauber, A 2021, Explainable cyber-physical energy systems based on knowledge graph. in Peter Palensky, Anurag Srivastava (ed.), MSCPES'21 : Proceedings of the 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems., 4, Association for Computing Machinery, New York, pp. 1 - 6, 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems, MSCPES 2021, Held as part of the Cyber-Physical Systems and Internet-of-Things Week 2021, Virtual, Online, United States, 18/05/21. https://doi.org/10.1145/3470481.3472704

Explainable cyber-physical energy systems based on knowledge graph. / Aryan, Peb Ruswono; Ekaputra, Fajar J.; Sabou, Reka Marta et al.
MSCPES'21 : Proceedings of the 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems. ed. / Peter Palensky, Anurag Srivastava. New York: Association for Computing Machinery, 2021. p. 1 - 6 4.

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

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T1 - Explainable cyber-physical energy systems based on knowledge graph

AU - Aryan, Peb Ruswono

AU - Ekaputra, Fajar J.

AU - Sabou, Reka Marta

AU - Hauer, Daniel

AU - Mosshammer, Ralf

AU - Einfalt, Alfred

AU - Miksa, Tomasz

AU - Rauber, Andreas

PY - 2021/5/19

Y1 - 2021/5/19

N2 - Explainability can help cyber-physical systems alleviating risk in automating decisions that are affecting our life. Building an explainable cyber-physical system requires deriving explanations from system events and causality between the system elements. Cyber-physical energy systems such as smart grids involve cyber and physical aspects of energy systems and other elements, namely social and economic. Moreover, a smart-grid scale can range from a small village to a large region across countries. Therefore, integrating these varieties of data and knowledge is a fundamental challenge to build an explainable cyber-physical energy system. This paper aims to use knowledge graph based framework to solve this challenge. The framework consists of an ontology to model and link data from various sources and graph-based algorithm to derive explanations from the events. A simulated demand response scenario covering the above aspects further demonstrates the applicability of this framework.

AB - Explainability can help cyber-physical systems alleviating risk in automating decisions that are affecting our life. Building an explainable cyber-physical system requires deriving explanations from system events and causality between the system elements. Cyber-physical energy systems such as smart grids involve cyber and physical aspects of energy systems and other elements, namely social and economic. Moreover, a smart-grid scale can range from a small village to a large region across countries. Therefore, integrating these varieties of data and knowledge is a fundamental challenge to build an explainable cyber-physical energy system. This paper aims to use knowledge graph based framework to solve this challenge. The framework consists of an ontology to model and link data from various sources and graph-based algorithm to derive explanations from the events. A simulated demand response scenario covering the above aspects further demonstrates the applicability of this framework.

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ER -

Aryan PR, Ekaputra FJ , Sabou RM, Hauer D, Mosshammer R, Einfalt A et al. Explainable cyber-physical energy systems based on knowledge graph. In Peter Palensky, Anurag Srivastava, editor, MSCPES'21 : Proceedings of the 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems. New York: Association for Computing Machinery. 2021. p. 1 - 6. 4 doi: 10.1145/3470481.3472704

Explainable cyber-physical energy systems based on knowledge graph

Abstract

Conference

Bibliographical note

Austrian Classification of Fields of Science and Technology (ÖFOS)

Keywords

Access to Document

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