IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v221y2018icp220-228.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918304707
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.03.132?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Khosrowpour, Ardalan & Xie, Yimeng & Taylor, John E. & Hong, Yili, 2016. "One size does not fit all: Establishing the need for targeted eco-feedback," Applied Energy, Elsevier, vol. 184(C), pages 523-530.
    2. Anderson, Kyle & Song, Kwonsik & Lee, SangHyun & Krupka, Erin & Lee, Hyunsoo & Park, Moonseo, 2017. "Longitudinal analysis of normative energy use feedback on dormitory occupants," Applied Energy, Elsevier, vol. 189(C), pages 623-639.
    3. Simcock, Neil & MacGregor, Sherilyn & Catney, Philip & Dobson, Andrew & Ormerod, Mark & Robinson, Zoe & Ross, Simon & Royston, Sarah & Marie Hall, Sarah, 2014. "Factors influencing perceptions of domestic energy information: Content, source and process," Energy Policy, Elsevier, vol. 65(C), pages 455-464.
    4. Ellegård, Kajsa & Palm, Jenny, 2011. "Visualizing energy consumption activities as a tool for making everyday life more sustainable," Applied Energy, Elsevier, vol. 88(5), pages 1920-1926, May.
    5. Costa, Andrea & Keane, Marcus M. & Torrens, J. Ignacio & Corry, Edward, 2013. "Building operation and energy performance: Monitoring, analysis and optimisation toolkit," Applied Energy, Elsevier, vol. 101(C), pages 310-316.
    6. Buchanan, Kathryn & Russo, Riccardo & Anderson, Ben, 2015. "The question of energy reduction: The problem(s) with feedback," Energy Policy, Elsevier, vol. 77(C), pages 89-96.
    7. Vassileva, Iana & Dahlquist, Erik & Wallin, Fredrik & Campillo, Javier, 2013. "Energy consumption feedback devices’ impact evaluation on domestic energy use," Applied Energy, Elsevier, vol. 106(C), pages 314-320.
    8. Gulbinas, R. & Jain, R.K. & Taylor, J.E., 2014. "BizWatts: A modular socio-technical energy management system for empowering commercial building occupants to conserve energy," Applied Energy, Elsevier, vol. 136(C), pages 1076-1084.
    9. Eguaras-Martínez, María & Vidaurre-Arbizu, Marina & Martín-Gómez, César, 2014. "Simulation and evaluation of Building Information Modeling in a real pilot site," Applied Energy, Elsevier, vol. 114(C), pages 475-484.
    10. Delmas, Magali A. & Fischlein, Miriam & Asensio, Omar I., 2013. "Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012," Energy Policy, Elsevier, vol. 61(C), pages 729-739.
    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. Wang, Wei & Hong, Tianzhen & Li, Nan & Wang, Ryan Qi & Chen, Jiayu, 2019. "Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification," Applied Energy, Elsevier, vol. 236(C), pages 55-69.
    2. Troy Malatesta & Gregory M. Morrison & Jessica K. Breadsell & Christine Eon, 2023. "A Systematic Literature Review of the Interplay between Renewable Energy Systems and Occupant Practices," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    3. Xun Liu & Zhenhan Ding & Xiaobo Li & Zhiyuan Xue, 2023. "Research Progress, Hotspots, and Trends of Using BIM to Reduce Building Energy Consumption: Visual Analysis Based on WOS Database," IJERPH, MDPI, vol. 20(4), pages 1-21, February.
    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).
    5. 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.
    6. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
    7. Chou, Jui-Sheng & Truong, Ngoc-Son, 2019. "Cloud forecasting system for monitoring and alerting of energy use by home appliances," Applied Energy, Elsevier, vol. 249(C), pages 166-177.
    8. 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).
    9. 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.
    10. 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).
    11. 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).
    12. 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).
    13. Francisco, Abigail & Taylor, John E., 2019. "Understanding citizen perspectives on open urban energy data through the development and testing of a community energy feedback system," Applied Energy, Elsevier, vol. 256(C).
    14. Graeme Stuart & Leticia Ozawa-Meida, 2020. "Supporting Decentralised Energy Management through Smart Monitoring Systems in Public Authorities," Energies, MDPI, vol. 13(20), pages 1-20, October.
    15. 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.
    16. Sherif Goubran & Carmela Cucuzzella & Mohamed M. Ouf, 2021. "Eyes on the Goal! Exploring Interactive Artistic Real-Time Energy Interfaces for Target-Specific Actions in the Built Environment," Sustainability, MDPI, vol. 13(4), pages 1-17, February.

    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. Khosrowpour, Ardalan & Jain, Rishee K. & Taylor, John E. & Peschiera, Gabriel & Chen, Jiayu & Gulbinas, Rimas, 2018. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation," Applied Energy, Elsevier, vol. 218(C), pages 304-316.
    2. Zhang, Chaoqun & Zha, Donglan & Jiang, Pansong & Wang, Fu & Yang, Guanglei & Salman, Muhammad & Wu, Qing, 2023. "The effect of customized information feedback on individual electricity saving behavior: Evidence from a field experiment in China," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Chatzigeorgiou, I.M. & Andreou, G.T., 2021. "A systematic review on feedback research for residential energy behavior change through mobile and web interfaces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. Valor, Carmen & Escudero, Carmen & Labajo, Victoria & Cossent, Rafael, 2019. "Effective design of domestic energy efficiency displays: A proposed architecture based on empirical evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Zha, Donglan & Zhang, Chaoqun & Jiang, Pansong & Wang, Fu, 2024. "What makes energy consumption behavior visible? Conceptualization, scale development and validation of customized information feedback," Journal of Business Research, Elsevier, vol. 182(C).
    7. Khosrowpour, Ardalan & Xie, Yimeng & Taylor, John E. & Hong, Yili, 2016. "One size does not fit all: Establishing the need for targeted eco-feedback," Applied Energy, Elsevier, vol. 184(C), pages 523-530.
    8. Shen, Meng & Young, Robert & Cui, Qingbin, 2016. "The normative feedback approach for energy conservation behavior in the military community," Energy Policy, Elsevier, vol. 98(C), pages 19-32.
    9. Weber, Sylvain & Puddu, Stefano & Pacheco, Diana, 2017. "Move it! How an electric contest motivates households to shift their load profile," Energy Economics, Elsevier, vol. 68(C), pages 255-270.
    10. Nilsson, Andreas & Bergstad, Cecilia Jakobsson & Thuvander, Liane & Andersson, David & Andersson, Kristin & Meiling, Pär, 2014. "Effects of continuous feedback on households’ electricity consumption: Potentials and barriers," Applied Energy, Elsevier, vol. 122(C), pages 17-23.
    11. Penelope Buckley, 2020. "Prices, information and nudges for residential electricity conservation : A meta-analysis," Post-Print hal-02500507, HAL.
    12. Jianling Jiao & Nuonuo Chen & Ranran Yang, 2024. "How to promote green travel effectively: a study of niche information interventions based on meta-analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(4), pages 8267-8301, April.
    13. Pothitou, Mary & Hanna, Richard F. & Chalvatzis, Konstantinos J., 2016. "Environmental knowledge, pro-environmental behaviour and energy savings in households: An empirical study," Applied Energy, Elsevier, vol. 184(C), pages 1217-1229.
    14. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    15. Walter Salas-Zapata & Anny Posada-Castaño & Diana Mejía-Durango, 2021. "An explanation of the behavioral origin of moderation in the use of natural resources: a meta-synthesis study," Environment Systems and Decisions, Springer, vol. 41(4), pages 487-500, December.
    16. Kendel, Adnane & Lazaric, Nathalie & Maréchal, Kevin, 2017. "What do people ‘learn by looking’ at direct feedback on their energy consumption? Results of a field study in Southern France," Energy Policy, Elsevier, vol. 108(C), pages 593-605.
    17. Schleich, Joachim & Faure, Corinne & Klobasa, Marian, 2017. "Persistence of the effects of providing feedback alongside smart metering devices on household electricity demand," Energy Policy, Elsevier, vol. 107(C), pages 225-233.
    18. Vassileva, Iana & Campillo, Javier, 2014. "Increasing energy efficiency in low-income households through targeting awareness and behavioral change," Renewable Energy, Elsevier, vol. 67(C), pages 59-63.
    19. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    20. Fettermann, Diego Castro & Cavalcante, Caroline Gobbo Sá & Ayala, Néstor Fabián & Avalone, Marianne Costa, 2020. "Configuration of a smart meter for Brazilian customers," Energy Policy, Elsevier, vol. 139(C).

    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:eee:appene:v:221:y:2018:i:c:p:220-228. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.