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Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

Author

Listed:
  • Marco Pritoni

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Drew Paine

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Gabriel Fierro

    (Electrical Engineering & Computer Sciences Department, University of California, Berkeley, CA 94720, USA)

  • Cory Mosiman

    (National Renewable Energy Laboratory, Golden, CO 80401, USA)

  • Michael Poplawski

    (Pacific Northwest National Laboratory, Richland, WA 99354, USA)

  • Avijit Saha

    (National Renewable Energy Laboratory, Golden, CO 80401, USA)

  • Joel Bender

    (Building Automation and Control Systems Integration Group, Cornell University, Ithaca, NY 14850, USA)

  • Jessica Granderson

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

Abstract

Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

Suggested Citation

  • Marco Pritoni & Drew Paine & Gabriel Fierro & Cory Mosiman & Michael Poplawski & Avijit Saha & Joel Bender & Jessica Granderson, 2021. "Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis," Energies, MDPI, vol. 14(7), pages 1-37, April.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:2024-:d:531075
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    References listed on IDEAS

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    Cited by:

    1. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    2. Cory Mosiman & Gregor Henze & Herbert Els, 2021. "Development and Application of Schema Based Occupant-Centric Building Performance Metrics," Energies, MDPI, vol. 14(12), pages 1-16, June.
    3. Khan Rahmat Ullah & Marudhappan Thirugnanasambandam & Rahman Saidur & Kazi Akikur Rahman & Md. Riaz Kayser, 2021. "Analysis of Energy Use and Energy Savings: A Case Study of a Condiment Industry in India," Energies, MDPI, vol. 14(16), pages 1-25, August.
    4. Luo, Na & Pritoni, Marco & Hong, Tianzhen, 2021. "An overview of data tools for representing and managing building information and performance data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    5. Yimin Chen & Guanjing Lin & Eliot Crowe & Jessica Granderson, 2021. "Development of a Unified Taxonomy for HVAC System Faults," Energies, MDPI, vol. 14(17), pages 1-25, September.
    6. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
    7. 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.
    8. Gardian, H. & Beck, J.-P. & Koch, M. & Kunze, R. & Muschner, C. & Hülk, L. & Bucksteeg, M., 2022. "Data harmonisation for energy system analysis – Example of multi-model experiments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    9. Filippos Lygerakis & Nikos Kampelis & Dionysia Kolokotsa, 2022. "Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review," Energies, MDPI, vol. 15(20), pages 1-32, October.
    10. Sulzer, Matthias & Wetter, Michael & Mutschler, Robin & Sangiovanni-Vincentelli, Alberto, 2023. "Platform-based design for energy systems," Applied Energy, Elsevier, vol. 352(C).

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