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

Marco Pau, Panagiotis Kapsalis, Zhiyu Pan, George Korbakis, Dario Pellegrino and Antonello Monti
Additional contact information
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

Energies, 2022, vol. 15, issue 7, 1-22

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.

Keywords: high-level architecture; building services; building value chain; big data; Internet of Things; data analytics (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/1996-1073/15/7/2568/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/7/2568/ (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:7:p:2568-:d:784979

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 ().