IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i7p2024-d531075.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/7/2024/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/7/2024/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    2. Blum, D.H. & Arendt, K. & Rivalin, L. & Piette, M.A. & Wetter, M. & Veje, C.T., 2019. "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems," Applied Energy, Elsevier, vol. 236(C), pages 410-425.
    3. Janna Hastings & Leonid Chepelev & Egon Willighagen & Nico Adams & Christoph Steinbeck & Michel Dumontier, 2011. "The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-13, October.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    2. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
    3. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    4. İlkay Unay-Gailhard & Mark A. Brennen, 2022. "How digital communications contribute to shaping the career paths of youth: a review study focused on farming as a career option," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 39(4), pages 1491-1508, December.
    5. Mahin Ghafari & Vali Baigi & Zahra Cheraghi & Amin Doosti-Irani, 2016. "The Prevalence of Asymptomatic Bacteriuria in Iranian Pregnant Women: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-10, June.
    6. Elizabeth T Cafiero-Fonseca & Andrew Stawasz & Sydney T Johnson & Reiko Sato & David E Bloom, 2017. "The full benefits of adult pneumococcal vaccination: A systematic review," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-23, October.
    7. Santos Urbina & Sofía Villatoro & Jesús Salinas, 2021. "Self-Regulated Learning and Technology-Enhanced Learning Environments in Higher Education: A Scoping Review," Sustainability, MDPI, vol. 13(13), pages 1-12, June.
    8. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    9. Oded Berger-Tal & Alison L Greggor & Biljana Macura & Carrie Ann Adams & Arden Blumenthal & Amos Bouskila & Ulrika Candolin & Carolina Doran & Esteban Fernández-Juricic & Kiyoko M Gotanda & Catherine , 2019. "Systematic reviews and maps as tools for applying behavioral ecology to management and policy," Behavioral Ecology, International Society for Behavioral Ecology, vol. 30(1), pages 1-8.
    10. Nadine Desrochers & Adèle Paul‐Hus & Jen Pecoskie, 2017. "Five decades of gratitude: A meta‐synthesis of acknowledgments research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2821-2833, December.
    11. Maryono, Maryono & Killoes, Aditya Marendra & Adhikari, Rajendra & Abdul Aziz, Ammar, 2024. "Agriculture development through multi-stakeholder partnerships in developing countries: A systematic literature review," Agricultural Systems, Elsevier, vol. 213(C).
    12. Alene Sze Jing Yong & Yi Heng Lim & Mark Wing Loong Cheong & Ednin Hamzah & Siew Li Teoh, 2022. "Willingness-to-pay for cancer treatment and outcome: a systematic review," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 1037-1057, August.
    13. Xue-Ying Xu & Hong Kong & Rui-Xiang Song & Yu-Han Zhai & Xiao-Fei Wu & Wen-Si Ai & Hong-Bo Liu, 2014. "The Effectiveness of Noninvasive Biomarkers to Predict Hepatitis B-Related Significant Fibrosis and Cirrhosis: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-16, June.
    14. Vicente Miñana-Signes & Manuel Monfort-Pañego & Javier Valiente, 2021. "Teaching Back Health in the School Setting: A Systematic Review of Randomized Controlled Trials," IJERPH, MDPI, vol. 18(3), pages 1-18, January.
    15. Agnieszka A. Tubis & Katarzyna Grzybowska, 2022. "In Search of Industry 4.0 and Logistics 4.0 in Small-Medium Enterprises—A State of the Art Review," Energies, MDPI, vol. 15(22), pages 1-26, November.
    16. Obsa Urgessa Ayana & Jima Degaga, 2022. "Effects of rural electrification on household welfare: a meta-regression analysis," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 69(2), pages 209-261, June.
    17. Caloffi, Annalisa & Colovic, Ana & Rizzoli, Valentina & Rossi, Federica, 2023. "Innovation intermediaries' types and functions: A computational analysis of the literature," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    18. García-Poole, Chloe & Byrne, Sonia & Rodrigo, María José, 2019. "How do communities intervene with adolescents at psychosocial risk? A systematic review of positive development programs," Children and Youth Services Review, Elsevier, vol. 99(C), pages 194-209.
    19. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    20. Qing Ye & Bao-Xin Qian & Wei-Li Yin & Feng-Mei Wang & Tao Han, 2016. "Association between the HFE C282Y, H63D Polymorphisms and the Risks of Non-Alcoholic Fatty Liver Disease, Liver Cirrhosis and Hepatocellular Carcinoma: An Updated Systematic Review and Meta-Analysis o," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:2024-:d:531075. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.