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Three-Dimensional Visualization Solution to Building-Energy Diagnosis for Energy Feedback

Author

Listed:
  • Tae-Keun Oh

    (Department of Safety Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

  • Donghwan Lee

    (Department of Convergence Engineering for Future City, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyeonggi-do, Korea)

  • Minsoo Park

    (Department of Civil, Architectural and Environmental System Engineering, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyeonggi-do, Korea)

  • Gichun Cha

    (Department of Convergence Engineering for Future City, Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyeonggi-do, Korea)

  • Seunghee Park

    (School of Civil, Architectural Engineering and Landscape Architecture Sungkyunkwan University, 2066, Seobu-ro, Suwon-si 16419, Gyeonggi-do, Korea)

Abstract

Owing to the large ratio of consumption in the building sector, energy-saving strategies are required. Energy feedback is an energy-saving strategy that prompts consumers to change their energy-consumption behaviors. The strategy has been principally focused on providing energy-consumption information. However, the realization of energy savings using only consumption information remains limited. In this paper, a building-energy, three-dimensional (3D) visualization solution is thus proposed. The aim is to determine if the building manager will replace the facility after our recommendation to improve the building-energy efficiency derived from the energy information is given. This solution includes the process of diagnosing a building and providing a prediction of energy requirements if a building improvement effort is undertaken. Accurate diagnostic information is provided by real-time measurement data from sensors and building models using a close-range photogrammetry method, without depending on blueprints. The information is provided by employing visualization effects to increase the energy-feedback efficiency. The proposed strategy is implemented on two testbeds, and building diagnostics are performed accordingly. For the first testbed, the predicted energy improvement amount resulting from the facility upgrade is provided. The second testbed is provided with a 3D visualization of the energy information. The predicted value of energy improvement was derived from the improvement plan through energy diagnosis in each testbed as about 30% and as about 28%, respectively. Unlike existing systems, which provide only ambiguous data that lack quantitative information, this study is meaningful because it provides energy information with the aid of visualization effects before and after building improvements.

Suggested Citation

  • Tae-Keun Oh & Donghwan Lee & Minsoo Park & Gichun Cha & Seunghee Park, 2018. "Three-Dimensional Visualization Solution to Building-Energy Diagnosis for Energy Feedback," Energies, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1736-:d:155821
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    References listed on IDEAS

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    Cited by:

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    2. Mahendra Singh & Nguyen Trung Kien & Houda Najeh & Stéphane Ploix & Antoine Caucheteux, 2019. "Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test," Energies, MDPI, vol. 12(13), pages 1-22, June.
    3. Chalal, M.L. & Medjdoub, B. & Bezai, N. & Bull, R. & Zune, M., 2022. "Visualisation in energy eco-feedback systems: A systematic review of good practice," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    4. Byung Chang Kwag & Sanghee Han & Gil Tae Kim & Beobjeon Kim & Jong Yeob Kim, 2020. "Analysis of the Effects of Strengthening Building Energy Policy on Multifamily Residential Buildings in South Korea," Sustainability, MDPI, vol. 12(9), pages 1-20, April.
    5. Ofelia Vera-Piazzini & Massimiliano Scarpa & Fabio Peron, 2022. "Building Energy Simulation and Monitoring: A Review of Graphical Data Representation," Energies, MDPI, vol. 16(1), pages 1-26, December.

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