A methodology for digitally optimising energy consumption in buildings dataset
  • Description

    Globally, the building sector accounts for 40% of the total energy consumption and directly contributes to climate change. In Australia, the building sector contributes up to 20% of its total energy consumption, thus emphasising the relevance of efficient energy management. For this reason, the optimisation of energy consumption in buildings has received considerable attention in recent times. Notwithstanding, the available works in the literature on energy consumption optimisation have considered the building a static object neglecting its constant interactions with occupants. These building-occupants interactions present the structure as a dynamic object and thus, should be considered in improving the energy consumption. As a result of the static consideration of buildings, the majority of the works have experienced gaps between the predicted and the actual energy consumption resulting in inadequate energy improvements. Thus, the dynamic nature of buildings should be considered to adequately manage energy consumption and improve occupants’ comfort. To address this gap, this study aimed at developing a methodology for digitally optimising energy consumption and indoor environmental parameters (heat, light, and air quality) of buildings using digital twin (DT) technology for dynamic occupant interactions

    1. Stakeholder Interviews: This folder contains the verbatim interview transcripts with the stakeholders of the library, analysis and results. The transcripts contain sensitive information.

    2. Expert Interviews: This folder contains the verbatim interview transcripts, completed Likert scale questionnaire and results of the expert interviews. The transcripts contain sensitive information.

    3. Living Lab Data: This folder contains the exported sensors data on the indoor conditions (temperature, relative humidity, illumination, carbon dioxide and total volatile organic compounds concentration) of the group study rooms on level 01 of the Kingswood library.

    This data is not available for open publication due to ethics restrictions. To discuss the data, please contact the creator De-Graft Joe Opoku <19621409@student.westernsydney.edu.au> ORCID 0000-0003-2557-5268.


    • - Start
      Data publication title A methodology for digitally optimising energy consumption in buildings dataset
    • Description

      Globally, the building sector accounts for 40% of the total energy consumption and directly contributes to climate change. In Australia, the building sector contributes up to 20% of its total energy consumption, thus emphasising the relevance of efficient energy management. For this reason, the optimisation of energy consumption in buildings has received considerable attention in recent times. Notwithstanding, the available works in the literature on energy consumption optimisation have considered the building a static object neglecting its constant interactions with occupants. These building-occupants interactions present the structure as a dynamic object and thus, should be considered in improving the energy consumption. As a result of the static consideration of buildings, the majority of the works have experienced gaps between the predicted and the actual energy consumption resulting in inadequate energy improvements. Thus, the dynamic nature of buildings should be considered to adequately manage energy consumption and improve occupants’ comfort. To address this gap, this study aimed at developing a methodology for digitally optimising energy consumption and indoor environmental parameters (heat, light, and air quality) of buildings using digital twin (DT) technology for dynamic occupant interactions

      1. Stakeholder Interviews: This folder contains the verbatim interview transcripts with the stakeholders of the library, analysis and results. The transcripts contain sensitive information.

      2. Expert Interviews: This folder contains the verbatim interview transcripts, completed Likert scale questionnaire and results of the expert interviews. The transcripts contain sensitive information.

      3. Living Lab Data: This folder contains the exported sensors data on the indoor conditions (temperature, relative humidity, illumination, carbon dioxide and total volatile organic compounds concentration) of the group study rooms on level 01 of the Kingswood library.

      This data is not available for open publication due to ethics restrictions. To discuss the data, please contact the creator De-Graft Joe Opoku <19621409@student.westernsydney.edu.au> ORCID 0000-0003-2557-5268.


      • Data type dataset
    • Keywords
      • Energy Efficiency
      • Digital Twin (DT)
      • Building Information Modelling (BIM)
      • Internet of Things (IoT)
      • Adoption
      • Living Lab
      • Semiotic Representations
      • University Library
      • Best Practice Guidelines
    • Funding source
      • Western Sydney University Postgraduate Research Scholarship
    • Grant number(s)
      • -
      • FoR codes
      • 330201 - Automation and technology in building and construction
      • 330204 - Building information modelling and management
      • 330206 - Building science, technologies and systems
      SEO codes
    - Coverage
      Temporal (time) coverage
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      Spatial (location,mapping) coverage
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    - Data
      Data Locations

      Type Location Notes
      The Data Manager is: De-Graft Joe Opoku
      Access conditions Conditional
    - Supplements
    • Related publications
        Name A methodology for digitally optimising energy consumption in buildings
      • URL
      • Notes Thesis publication April 2024
        • Name Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M., Bamdad, K. and Famakinwa, T. (2024). Digital twin for indoor condition monitoring in living labs: University library case study, Automation in Construction, Vol. 157, 2024, 105188, ISSN: 0926-5805,
      • URL https://doi.org/10.1016/j.autcon.2023.105188
      • Notes
        • Name Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M., Bamdad, K. and Famakinwa, T. (2023). Barriers to the adoption of digital twin in the construction industry: A literature review, Informatics, Vol. 10, No.1, pp.14
      • URL https://doi.org/10.3390/informatics10010014
      • Notes
        • Name Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M., Famakinwa, T. and Bamdad, K. (2022). Drivers for digital twin adoption in the construction industry: A systematic literature review, Buildings, Vol. 12, No.2, pp.113.
      • URL https://doi.org/10.3390/buildings12020113
      • Notes
        • Name Opoku, D.G.J., Perera, S., Osei-Kyei, R. and Rashidi, M. (2021). Digital twin application in the construction industry: A literature review, Journal of Building Engineering, Vol. 40, August 2021, 102726.
      • URL https://doi.org/10.1016/j.jobe.2021.102726
      • Notes
        • Name Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M., Bamdad, K. and Famakinwa, T. (2022). Buildings indoor environmental conditions: A thematic analysis of verbatim comments from university library stakeholders. In the 45th Australasian Universities Building Education Association (AUBEA) conference. Sydney, Australia, November 23-25, 2022
      • URL
      • Notes
        • Name Opoku, D.G.J., Perera, S., Osei-Kyei, R. and Rashidi, M. (2022). Review of driving forces for digital twin adoption in the construction industry. In CIB World Building Congress 2022. Melbourne, Australia: CIB. June 27-30, 2022.
      • URL https://cibworld.org/book-of-abstracts-world-building-conference-2022/
      • Notes
        • Name Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M. and Bamdad, K. (2022). Obstacles to the implementation of digital twin: A review in the construction industry. In 9th International Workshop, When Social Science meets Lean and BIM: Towards Industry 5.0, 43. Western Sydney University, Australia.
      • URL https://doi.org/10.26183/1z3j-sx28.
      • Notes
        • Name Perera, S., Opoku, D-G.J. and Rodrigo, N. (2022). Technological advancements in green and sustainable construction. In E. Adinyira and K. Agyekum (Eds.), The Construction Industry: Global trends, job burnout and safety issues (Chapter 3). Nova Science Publishers.
      • URL
      • Notes ISBN: 9781685073381
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    - License
      The data will be licensed under
    • Other license
      • Statement of rights in data Copyright Western Sydney University
    - Citation
      Citation Opoku, De-Graft Joe; Perera, Ravi (2024): A methodology for digitally optimising energy consumption in buildings dataset. Western Sydney University. https://doi.org/10.26183/8y9w-mx48

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