Leveraging Graph Analytics for Energy Efficiency Certificates

Panagiotis Kapsalis, Giorgos Kormpakis, Konstantinos Alexakis and Dimitrios Askounis
Additional contact information
Panagiotis Kapsalis: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece
Giorgos Kormpakis: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece
Konstantinos Alexakis: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece
Dimitrios Askounis: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece

Energies, 2022, vol. 15, issue 4, 1-12

Abstract: As energy efficiency is becoming a subject of utter importance in today’s societies, the European Union and a vast number of organizations have put a lot of focus on it. As a result, huge amounts of data are generated at an unprecedented rate. After thorough analysis and exploration, these data could provide a variety of solutions and optimizations regarding the energy efficiency subject. However, all the potential solutions that could derive from the aforementioned procedures still remain untapped due to the fact that these data are yet fragmented and highly sophisticated. In this paper, we propose an architecture for a Reasoning Engine, a mechanism that provides intelligent querying, insights and search capabilities, by leveraging technologies that will be described below. The proposed architecture has been developed in the context of the H2020 project called MATRYCS. In this paper, the reasons that resulted from the need of efficient ways of querying and analyzing the large amounts of data are firstly explained. Subsequently, several use cases, where related technologies were used to address real-world challenges, are presented. The main focus, however, is put in the detailed presentation of our Reasoning Engine’s implementation steps. Lastly, the outcome of our work is demonstrated, showcasing the derived results and the optimizations that have been implemented.

Keywords: energy efficiency; semantics; reasoning engine; digital twin; big graph analytics; knowledge bases; data processing semantic enrichment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (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)
https://www.mdpi.com/1996-1073/15/4/1500/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/4/1500/ (text/html)

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:gam:jeners:v:15:y:2022:i:4:p:1500-:d:751873

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().