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Microservices Model to Enhance the Availability of Data for Buildings Energy Efficiency Management Services

Author

Listed:
  • Muhammad Aslam Jarwar

    (Department of Information and Communications Engineering, Hankuk University of Foreign Studies, Seoul 02450, Korea)

  • Sajjad Ali

    (Department of Information and Communications Engineering, Hankuk University of Foreign Studies, Seoul 02450, Korea)

  • Ilyoung Chong

    (Department of Information and Communications Engineering, Hankuk University of Foreign Studies, Seoul 02450, Korea)

Abstract

In the Internet of Things (IoT)-supported energy data management infrastructure, objects from various energy generation and consumption terminals in buildings produce a tremendous amount of data. However, this data is not useful unless it is available on-time for services that discover meaningful information in order to provide intelligent decisions. The microservices-based data caching, data virtualization, data processing, data analysis, and data ingestion methods can be applied to enhance the data availability for energy efficiency management services provision across buildings. To foster building energy efficiency management services (BEEMS), Web of Objects (WoO) provides data abstraction, aggregation, and ingestion mechanism with virtual objects (VOs) and composite virtual objects (CVOs) by using ontologies and availability and scalability of services with microservices. This article proposes the use of data processing microservices modeling to enhance data availability and expose services capabilities with microservices for BEEMS. We present a semantic web agent based on an ontology for linking, enhancement, reusability, and availability of data-objects, services, and microservices. For the evaluation, we present a use case, which includes heterogeneous data collection and processing and provision of various BEEMS. A prototype for the use case scenario has been built and the results have been evaluated in the laboratory to mimic the enhanced data availability for BEEMS.

Suggested Citation

  • Muhammad Aslam Jarwar & Sajjad Ali & Ilyoung Chong, 2019. "Microservices Model to Enhance the Availability of Data for Buildings Energy Efficiency Management Services," Energies, MDPI, vol. 12(3), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:360-:d:200295
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    References listed on IDEAS

    as
    1. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    2. Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
    3. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
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