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Leveraging Graph Analytics for Energy Efficiency Certificates

Author

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  • 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)

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.

Suggested Citation

  • Panagiotis Kapsalis & Giorgos Kormpakis & Konstantinos Alexakis & Dimitrios Askounis, 2022. "Leveraging Graph Analytics for Energy Efficiency Certificates," Energies, MDPI, vol. 15(4), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1500-:d:751873
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    References listed on IDEAS

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    1. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    2. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    3. Marinakis, Vangelis & Doukas, Haris & Karakosta, Charikleia & Psarras, John, 2013. "An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector," Applied Energy, Elsevier, vol. 101(C), pages 6-14.
    4. Helm, Dieter, 2014. "The European framework for energy and climate policies," Energy Policy, Elsevier, vol. 64(C), pages 29-35.
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    1. Giacomo Di Foggia & Massimo Beccarello & Marco Borgarello & Francesca Bazzocchi & Stefano Moscarelli, 2022. "Market-Based Instruments to Promote Energy Efficiency: Insights from the Italian Case," Energies, MDPI, vol. 15(20), pages 1-16, October.
    2. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.

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