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All issues Volume 111 (2019) E3S Web Conf., 111 (2019) 05017 References
Open Access
Issue
E3S Web Conf.
Volume 111, 2019
CLIMA 2019 Congress
Article Number 05017
Number of page(s) 5
Section Information and Communication Technologies (ICT) for the Intelligent Building Management
DOI https://doi.org/10.1051/e3sconf/201911105017
Published online 13 August 2019
  1. United Nations Framework Convention on Climate Change (ed.) Adoption of the Paris Agreement, number FCCC/CP/2015/L.9/Rev.1 [Google Scholar]
  2. Millar, D., Tonolo, G., Ziebinska, U. Energy efficiency indicators – HIGHLIGHTS(International Energy Agency, 2016) [Google Scholar]
  3. Lenzen, M. Künstliche Intelligenz, Was sie kann & was uns erwartet(C.H. Beck, 2018) [Google Scholar]
  4. Adey, R., Sriram, D. Applications of artificial intelligence to engineering problems, report 5113597, (1987) [Google Scholar]
  5. Kalogirou, S. A. Renew. Sust. Ener. Rev., 5, 373–401 (2001) [CrossRef] [Google Scholar]
  6. Magnier, L., Haghighat, F. Build. Env., 45, 739–746 (2010) [CrossRef] [Google Scholar]
  7. Kusiak, A.,Xu, G. Energy, 42, 241–250 (2012) [CrossRef] [Google Scholar]
  8. Mohanraj, M., Jayaraj, S., Muraleedharan, C. Renew. Sust. Ener. Rev., 16, 1340–1358 (2012) [CrossRef] [Google Scholar]
  9. Nassif, N. Build. Sim., 7, 237–245 (2014) [Google Scholar]
  10. Ahmad, M. W., Mourshed, M., Yuce, B., Rezgui, Y. Build. Sim., Springer Nature, 9, 359–398 (2016) [Google Scholar]
  11. Afram, A., Janabi-Sharifi, F., Fung, A. S., Raahemifar, K. Ener. and Build., 141, 96–113 (2017) [CrossRef] [Google Scholar]
  12. Liang, J., Du, R. Int. J. Refrig., 30, 1104–1114 (2007) [Google Scholar]
  13. Najafi, M. Fault Detection and Diagnosis in Building HVAC Systems(University of California, Berkely, PhD thesis, 2010) [Google Scholar]
  14. West, S. R., Guo, Y., Wang, X. R., Wall, J. Automated Fault Detection and Diagnosis of HVAC Subsystems Using Statistical Machine Learning. Proceedings of Building Simulation. 12th Conference of International Building Performance Simulation Association, Sydney, 14-16 November., 2011, 2659-2665 [Google Scholar]
  15. Narayanaswamy, B., Balaji, B., Gupta, R., Agarwal, Y. Data Driven Investigation of Faults in HVAC Systems with Model, Cluster and Compare (MCC). BuildSys’14, November 5–6, 2014, Memphis, TN, USA, 2014, 1-10 [Google Scholar]
  16. Du, Z., Fan, B., Jin, X., Chi, J. Build. Environ., 73, 1–11 (2014) [Google Scholar]
  17. Araya, D. B., Grolinger, K., ElYamany, H. F., Capretz, M. A., Bitsuamlak, G. Ener. Build., 144, 191–206 (2017) [CrossRef] [Google Scholar]
  18. Hantsch, A., Mai, R. Einfluss instationärer Raumluftströmung auf die thermische Behaglichkeit im Aufenthaltsbereich. CEGA-Congress für Experten der TGA, 27.–28.11. 2018, Baden-Baden, 2018 [Google Scholar]
  19. Kuchling, H. Taschenbuch der Physik, 16th Ed. (Fachbuchverlag Leipzig, 1996) [Google Scholar]
  20. Aris, R. Vectors, Tensors and the Basic Equations of Fluid Mechanics(Dover Publication Inc., 1990) [Google Scholar]
  21. Baehr, H. D. Thermodynamik(Springer-Verlag, 1992) [Google Scholar]
  22. Bowles, M. Machine Learning in Python(John Wiley & Sons Inc., 2015) [Google Scholar]
  23. Müller, A. C., Guido, S. Introduction to Machine Learning with Python: A Guide for Data Scientists(O’Reilly Media Inc., 2016) [Google Scholar]
  24. McKinney, W. Datenanalyse mit Python(Dpunkt.Verlag GmbH, 2015) [Google Scholar]
  25. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. J. Mach. Learn. R., 12, 2825–2830 (2011) [Google Scholar]
  26. Géron, A. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to build intelligent systems(O’Reilly Media Inc., 2017) [Google Scholar]

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