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Occupant perceptions of building information model-based energy visualizations in eco-feedback systems

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  • Francisco, Abigail
  • Truong, Hanh
  • Khosrowpour, Ardalan
  • Taylor, John E.
  • Mohammadi, Neda

Abstract

While technology advancements are improving the energy efficiency of buildings, occupant behavior remains a critical factor in ensuring the effectiveness of such enhancements. To this end, numerous eco-feedback systems have been developed to reduce building energy use through influencing occupants' behaviors during building operations. Information representation is a critical component in eco-feedback systems, affecting the users' interpretation, engagement, and motivation to reduce energy consumption. Many studies have focused on using different charts and technical units or abstract and artistic visualizations to represent energy consumption. However, the effectiveness of such techniques varies across studies. Recent research emphasizes the need to integrate information representation strategies that balance numeric and aesthetic appeal. Concurrently, studies have called for increased adoption of a Building Information Model (BIM) during a building's operations phase to improve facility management. In this paper, we introduce a new eco-feedback information representation method that combines numeric and aesthetic appeal through leveraging spatial and color-coding techniques in BIM. The BIM-integrated energy visualization approach developed in this paper uses the Revit Application Program Interface (API) and allows users to visualize and compare energy consumption values in 2D and 3D views of a multi-family building through a color-coding scheme in an as-built BIM. The method is validated through a user survey that quantitatively and qualitatively assesses the proposed 2D and 3D BIM eco-feedback compared to more traditional bar chart based eco-feedback. Our findings suggest that 2D spatial, color-coded eco-feedback provides the optimal information representation, as it is easy to understand, while evoking engaging and motivating responses from users. This study advances our understanding of eco-feedback information representation while expanding BIM applications during building operations. These are important steps to address the human dimension of energy efficiency in the built environment.

Suggested Citation

  • Francisco, Abigail & Truong, Hanh & Khosrowpour, Ardalan & Taylor, John E. & Mohammadi, Neda, 2018. "Occupant perceptions of building information model-based energy visualizations in eco-feedback systems," Applied Energy, Elsevier, vol. 221(C), pages 220-228.
    • Handle: RePEc:eee:appene:v:221:y:2018:i:c:p:220-228
    DOI: 10.1016/j.apenergy.2018.03.132
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    4. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
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    8. Hanna Mela & Juha Peltomaa & Marja Salo & Kirsi Mäkinen & Mikael Hildén, 2018. "Framing Smart Meter Feedback in Relation to Practice Theory," Sustainability, MDPI, vol. 10(10), pages 1-22, October.
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    10. 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).
    11. Li, Hong Xian & Li, Yan & Jiang, Boya & Zhang, Limao & Wu, Xianguo & Lin, Jingyi, 2020. "Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis," Renewable Energy, Elsevier, vol. 149(C), pages 1414-1423.
    12. Gao, Kangping & Xu, Xinxin & Jiao, Shengjie, 2022. "Prediction and visualization analysis of drilling energy consumption based on mechanism and data hybrid drive," Energy, Elsevier, vol. 261(PA).
    13. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    14. Wu, Xianguo & Li, Xinyi & Qin, Yawei & Xu, Wen & Liu, Yang, 2023. "Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions," Applied Energy, Elsevier, vol. 339(C).
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