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MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain

Author

Listed:
  • Marco Pau

    (Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany)

  • Panagiotis Kapsalis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece)

  • Zhiyu Pan

    (Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany)

  • George Korbakis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece)

  • Dario Pellegrino

    (Engineering Ingegneria Informatica S.p.A., 90146 Palermo, Italy)

  • Antonello Monti

    (Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany)

Abstract

The building sector is undergoing a deep transformation to contribute to meeting the climate neutrality goals set by policymakers worldwide. This process entails the transition towards smart energy-aware buildings that have lower consumptions and better efficiency performance. Digitalization is a key part of this process. A huge amount of data is currently generated by sensors, smart meters and a multitude of other devices and data sources, and this trend is expected to exponentially increase in the near future. Exploiting these data for different use cases spanning multiple application scenarios is of utmost importance to capture their full value and build smart and innovative building services. In this context, this paper presents a high-level architecture for big data management in the building domain which aims to foster data sharing, interoperability and the seamless integration of advanced services based on data-driven techniques. This work focuses on the functional description of the architecture, underlining the requirements and specifications to be addressed as well as the design principles to be followed. Moreover, a concrete example of the instantiation of such an architecture, based on open source software technologies, is presented and discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2568-:d:784979
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    References listed on IDEAS

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    Cited by:

    1. Zhiyu Pan & Guanchen Pan & Antonello Monti, 2022. "Semantic-Similarity-Based Schema Matching for Management of Building Energy Data," Energies, MDPI, vol. 15(23), pages 1-23, November.
    2. Cezar-Petre Simion & Cătălin-Alexandru Verdeș & Alexandra-Andreea Mironescu & Florin-Gabriel Anghel, 2023. "Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-30, February.

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