Open Access
Issue |
E3S Web of Conf.
Volume 562, 2024
BuildSim Nordic 2024
|
|
---|---|---|
Article Number | 10006 | |
Number of page(s) | 12 | |
Section | Digital Twin & Smart Buildings | |
DOI | https://doi.org/10.1051/e3sconf/202456210006 | |
Published online | 07 August 2024 |
- Balaji, B., Bhattacharya, A., Fierro, G., Gao, J., Gluck, J., Hong, D., ... & Whitehouse, K. (2016, November). Brick: Towards a unified metadata schema for buildings. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (pp. 41-50). [Google Scholar]
- Balaji, B., Bhattacharya, A., Fierro, G., Gao, J., Gluck, J., Hong, D., ... & Whitehouse, K. (2018). Brick: Metadata schema for portable smart building applications. Applied energy, 226, 1273-1292. [CrossRef] [Google Scholar]
- Chaudhary, G., New, J., Sanyal, J., Im, P., O’Neill, Z., & Garg, V. (2016). Evaluation of “Autotune” calibration against manual calibration of building energy models. Applied energy, 182, 115-134. [CrossRef] [Google Scholar]
- Dronkelaar, Chris van, Mark Dowson, Catalina Spataru, and Dejan Mumovic. 2016. “A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in NonDomestic Buildings.” Frontiers in Mechanical Engineering 1 (January): 1– 14. [Google Scholar]
- Ghiaus, C. (2006). Experimental estimation of building energy performance by robust regression. Energy and buildings, 38(6), 582-587. [CrossRef] [Google Scholar]
- Hong, T., Yang, L., Hill, D., & Feng, W. (2014). Data and analytics to inform energy retrofit of high performance buildings. Applied Energy, 126, 90-106. [CrossRef] [Google Scholar]
- Jensen, Per Anker. 2012. “Knowledge Transfer from Facilities Management to Building Projects: A Typology of Transfer Mechanisms.” Architectural Engineering and Design Management 8 (3): 170–79. [CrossRef] [Google Scholar]
- Johra, H., Schaffer, M., Chaudhary, G., Kazmi, H. S., Le Dréau, J., & Petersen, S. (2023). What Metrics Does the Building Energy Performance Community Use to Compare Dynamic Models?. In Proceedings of Building Simulation 2023: 18th Conference of International Building Performance Simulation Association. Shanghai, China, 4-6 September 2023. IBPSA. [Google Scholar]
- Lassen, N., Josefsen, T., & Goia, F. (2021). Design and in-field testing of a multi-level system for continuous subjective occupant feedback on indoor climate. Building and Environment, 189, 107535. [CrossRef] [Google Scholar]
- McBride, B. (2004). The resource description framework (RDF) and its vocabulary description language RDFS. In Handbook on ontologies (pp. 51-65). Berlin, Heidelberg: Springer Berlin Heidelberg. [CrossRef] [Google Scholar]
- Menezes, Anna Carolina, Andrew Cripps, Dino Bouchlaghem, and Richard Buswell. 2012. “Predicted vs. Actual Energy Performance of Non-Domestic Buildings: Using Post-Occupancy Evaluation Data to Reduce the Performance Gap.” Applied Energy 97: 355–64. [CrossRef] [Google Scholar]
- Minerva, R.; Crespi, N.; Farahbakhsh, R.; Awan, F.M. Artificial Intelligence and the Digital Twin: An Essential Combination. In The Digital Twin; Springer: Berlin/Heidelberg, Germany, 2023; pp. 299–336. [CrossRef] [Google Scholar]
- Ornetzeder, Michael, Magdalena Wicher, and Jürgen Suschek-Berger. 2016. “User Satisfaction and Well-Being in Energy Efficient Office Buildings: Evidence from Cutting-Edge Projects in Austria.” Energy and Buildings 118: 18–26. [CrossRef] [Google Scholar]
- Robinson, S. (2023). Exploring the relationship between simulation model accuracy and complexity. Journal of the Operational Research Society, 74(9), 1992-2011. [CrossRef] [Google Scholar]
- Saad, M. M., & Eicker, U. (2023). Investigating the reliability of building energy models: Comparative analysis of the impact of data pipelines and model complexities. Journal of Building Engineering, 71, 106511. [CrossRef] [Google Scholar]
- Sanyal, J., & New, J. (2013, May). Simulation and big data challenges in tuning building energy models. In 2013 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) (pp. 1-6). IEEE. [Google Scholar]
- Sun, J., & Reddy, T. A. (2006). Calibration of building energy simulation programs using the analytic optimization approach (RP-1051). HVAC&R Research, 12(1), 177-196. [CrossRef] [Google Scholar]
- Tuegel, E. J., Ingraffea, A. R., Eason, T. G., and Spottswood, S. M., 2011, “Reengineering Aircraft Structural Life Prediction Using a Digital Twin,” Int. J. Aerosp. Eng., 2011, pp. 1–14. [Google Scholar]
- Wagg, D. & Worden, Keith & Barthorpe, Robert & Gardner, Paul. (2020). Digital Twins: State-of-The-Art Future Directions for Modelling and Simulation in Engineering Dynamics Applications. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg. 6. [Google Scholar]
- Zou, P. X., Xu, X., Sanjayan, J., & Wang, J. (2018). Review of 10 years research on building energy performance gap: Life-cycle and stakeholder perspectives. Energy and Buildings, 178, 165-181. [CrossRef] [Google Scholar]
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