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Big Data for Energy Management and Energy-Efficient Buildings

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  • Vangelis Marinakis

    (Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou str., 15773 Athens, Greece)

Abstract

European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.

Suggested Citation

  • Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1555-:d:337630
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    13. A-Ru-Han Bao & Yao Liu & Jun Dong & Zheng-Peng Chen & Zhen-Jie Chen & Chen Wu, 2022. "Evolutionary Game Analysis of Co-Opetition Strategy in Energy Big Data Ecosystem under Government Intervention," Energies, MDPI, vol. 15(6), pages 1-24, March.
    14. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
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    16. Paweł Dymora & Mirosław Mazurek & Bartosz Sudek, 2021. "Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service," Energies, MDPI, vol. 14(22), pages 1-23, November.
    17. Alexey I. Shinkevich & Yuri Yu. Kostyukhin & Diana Yu. Savon & Andrey E. Safronov & Alexander V. Aleksakhin, 2021. "Optimization of Energy-Efficient Functioning of the Oil and Gas Sector of the Economy through Digitalization and Resource Conservation," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 321-330.
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