IDEAS home Printed from https://ideas.repec.org/a/eee/recore/v123y2017icp219-229.html
   My bibliography  Save this article

Spatiotemporal analysis and visualization of power consumption data integrated with building information models for energy savings

Author

Listed:
  • Chou, Chien-Cheng
  • Chiang, Cheng-Ting
  • Wu, Pai-Yu
  • Chu, Chun-Ping
  • Lin, Chia-Ying

Abstract

Using a visualization engine to display the analyze results of power consumption data in a building can provide immediate and informative feedback for energy conservation research. Previous research has demonstrated that change of residents’ behavior can facilitate achieving the net-zero energy goal for a building. This research proposed a system called iARTS (interactive Augmented Reality system for Temporal and Spatial analysis of power consumption data integrated with building information models) that was designed to: (1) integrate building information model data into power consumption data sets in order to visualize the analysis results in Unity, which is a visualization engine originally designed for game development; (2) perform a spatiotemporal analysis mechanism to help residents realize an energy-saving tip, by identifying the appliances to be turned off; (3) perform another spatiotemporal analysis mechanism to identify the appliances that can be used jointly in order to consume all the solar PV-generated electricity at a maximum; (4) provide residents with query forms, scenes retrieval functions, and animations to educate residents as to where and when to implement the aforementioned energy-saving tips. With the use of iARTS, the temporal relationships between power sockets and appliances can be accurately described along with timestamped power consumption data. Residents are expected to be able to identify the electricity usage patterns that are wasteful, as well as to see any potential adjustment plan for using as much generated electricity as possible.

Suggested Citation

  • Chou, Chien-Cheng & Chiang, Cheng-Ting & Wu, Pai-Yu & Chu, Chun-Ping & Lin, Chia-Ying, 2017. "Spatiotemporal analysis and visualization of power consumption data integrated with building information models for energy savings," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 219-229.
    • Handle: RePEc:eee:recore:v:123:y:2017:i:c:p:219-229
    DOI: 10.1016/j.resconrec.2016.03.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0921344916300428
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resconrec.2016.03.008?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. Ohtomo, Shoji & Ohnuma, Susumu, 2014. "Psychological interventional approach for reduce resource consumption: Reducing plastic bag usage at supermarkets," Resources, Conservation & Recycling, Elsevier, vol. 84(C), pages 57-65.
    2. Ueno, Tsuyoshi & Sano, Fuminori & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data," Applied Energy, Elsevier, vol. 83(2), pages 166-183, February.
    Full references (including those not matched with items on IDEAS)

    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. Vega, A.M. & Santamaria, F. & Rivas, E., 2015. "Modeling for home electric energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 948-959.
    2. Khalid Mehmood & Fauzia Jabeen & Shilpa Iyanna & Hui Zhang & Patrice Piccardi & Nagendra Singh Nehra, 2024. "Exploring drivers of behavioral willingness to reduce plastic consumption and socially responsible consumption behavior: A stimulus–organism–behavior–consequence paradigm based environmental managemen," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(4), pages 3098-3118, July.
    3. Penelope Buckley, 2020. "Prices, information and nudges for residential electricity conservation : A meta-analysis," Post-Print hal-02500507, HAL.
    4. Oropeza-Perez, Ivan & Østergaard, Poul Alberg, 2014. "Potential of natural ventilation in temperate countries – A case study of Denmark," Applied Energy, Elsevier, vol. 114(C), pages 520-530.
    5. 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.
    6. 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.
    7. 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.
    8. Qadeer Ali & Muhammad Jamaluddin Thaheem & Fahim Ullah & Samad M. E. Sepasgozar, 2020. "The Performance Gap in Energy-Efficient Office Buildings: How the Occupants Can Help?," Energies, MDPI, vol. 13(6), pages 1-27, March.
    9. Peeraya Inyim & Mostafa Batouli & Maria Presa Reyes & Triana Carmenate & Leonardo Bobadilla & Ali Mostafavi, 2018. "A Smartphone Application for Personalized and Multi-Method Interventions toward Energy Saving in Buildings," Sustainability, MDPI, vol. 10(6), pages 1-19, May.
    10. Seyed Amin Tabatabaei & Wim Van der Ham & Michel C. A. Klein & Jan Treur, 2017. "A Data Analysis Technique to Estimate the Thermal Characteristics of a House," Energies, MDPI, vol. 10(9), pages 1-19, September.
    11. Azar, Elie & Menassa, Carol C., 2014. "A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks," Energy Policy, Elsevier, vol. 67(C), pages 459-472.
    12. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
    13. Olmos, Luis & Ruester, Sophia & Liong, Siok-Jen & Glachant, Jean-Michel, 2011. "Energy efficiency actions related to the rollout of smart meters for small consumers, application to the Austrian system," Energy, Elsevier, vol. 36(7), pages 4396-4409.
    14. Yue, Ting & Long, Ruyin & Chen, Hong, 2013. "Factors influencing energy-saving behavior of urban households in Jiangsu Province," Energy Policy, Elsevier, vol. 62(C), pages 665-675.
    15. Subramanyam, Veena & Kumar, Amit & Talaei, Alireza & Mondal, Md. Alam Hossain, 2017. "Energy efficiency improvement opportunities and associated greenhouse gas abatement costs for the residential sector," Energy, Elsevier, vol. 118(C), pages 795-807.
    16. Zhao, Liang & Zhang, Jili, 2015. "Research on the data transmission optimization for building energy consumption monitoring system based on fuzzy self-adaptation method," Energy, Elsevier, vol. 93(P2), pages 1385-1393.
    17. Yong Li & Bairong Wang, 2021. "Go Green and Recycle: Analyzing the Usage of Plastic Bags for Shopping in China," IJERPH, MDPI, vol. 18(23), pages 1-10, November.
    18. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    19. Murtagh, Niamh & Nati, Michele & Headley, William R. & Gatersleben, Birgitta & Gluhak, Alexander & Imran, Muhammad Ali & Uzzell, David, 2013. "Individual energy use and feedback in an office setting: A field trial," Energy Policy, Elsevier, vol. 62(C), pages 717-728.
    20. Musiliu 0. Oseni & Michael G. Poilitt & David M. Retner & Laura-Lucia Richter & Kong Chyong, 2013. "2013 EPRG Public Opinion Survey: Smart Energy Survey — Attitudes and Behaviours," Cambridge Working Papers in Economics 1352, Faculty of Economics, University of Cambridge.

    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:recore:v:123:y:2017:i:c:p:219-229. 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: Kai Meng (email available below). General contact details of provider: https://www.journals.elsevier.com/resources-conservation-and-recycling .

    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.