Open Access
Issue
E3S Web Conf.
Volume 336, 2022
The International Conference on Energy and Green Computing (ICEGC’2021)
Article Number 00040
Number of page(s) 15
DOI https://doi.org/10.1051/e3sconf/202233600040
Published online 17 January 2022
  1. M. LLC, Energy Policies beyond IEA Countries: Morocco 2019 (2019), https://www.iea.org/ [Google Scholar]
  2. J. Page, D. Robinson, N. Morel, J.L. Scartezzini, A generalised stochastic model for the simulation of occupant presence (2008), Vol. 40, pp. 83–98, ISSN 0378-7788 [Google Scholar]
  3. G. Singla, D. Cook, M. Schmitter-Edgecombe, Recognizing Independent and Joint Activities Among Multiple Residents in Smart Environments (2010), Vol. 1, pp. 57–63 [Google Scholar]
  4. L. ORTA, Réalisation d’un logiciel pour l’optimisation énergétique du bâtiment (2016), p. 48 [Google Scholar]
  5. F. Pallonetto, M. De Rosa, F. Milano, D.P. Finn, Demand response algorithms for smart-grid ready residential buildings using machine learning models (2019), Vol. 239, pp. 1265–1282, ISSN 0306-2619 [Google Scholar]
  6. Y. Peng, A. Rysanek, Z. Nagy, A. Schlüter, Using machine learning techniques for occupancy-prediction-based cooling control in office buildings (2018), Vol. 211, pp. 1343–1358, ISSN 0306-2619 [Google Scholar]
  7. C. Fan, F. Xiao, Y. Zhao, A short-term building cooling load prediction method using deep learning algorithms (2017), Vol. 195, pp. 222–233, ISSN 0306-2619 [Google Scholar]
  8. J.L. Gomez Ortega, L. Han, N. Whittacker, N. Bowring, A machine-learning based approach to model user occupancy and activity patterns for energy saving in buildings, in 2015 Science and Information Conference (SAI) (2015), pp. 474–482 [CrossRef] [Google Scholar]
  9. T. Yu, Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings, in 2010 Ninth International Conference on Machine Learning and Applications (2010), pp. 726–731 [CrossRef] [Google Scholar]
  10. B. Dong, B. Andrews, K.P. Lam, M. Höynck, R. Zhang, Y.S. Chiou, D. Benitez, An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network (2010), Vol. 42, pp. 1038–1046, ISSN 0378-7788 [Google Scholar]
  11. T. Ekwevugbe, N. Brown, V. Pakka, D. Fan, Real-time building occupancy sensing using neural-network based sensor network, in 2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST) (2013), pp. 114–119 [CrossRef] [Google Scholar]
  12. N. Li, G. Calis, B. Becerik-Gerber, Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations (2012), Vol. 24, pp. 89–99, ISSN 0926-5805 [Google Scholar]
  13. H. Hajj, W. El-Hajj, M. Dabbagh, T.R. Arabi, An Algorithm-Centric Energy-Aware Design Methodology (2014), Vol. 22, pp. 2431–2435 [Google Scholar]
  14. S. Mamidi, Y.H. Chang, R. Maheswaran, Improving Building Energy Efficiency with a Network of Sensing, Learning and Prediction Agents (2012) [Google Scholar]
  15. M.A. Ahajjam, D. Bonilla Licea, C. Essayeh, M. Ghogho, A. Kobbane, MORED: A Moroccan Buildings’ Electricity Consumption Dataset (2020), Vol. 13, ISSN 1996-1073 [Google Scholar]
  16. J. Han, J. Pei, M. Kamber, Data Mining: Concepts and Techniques (Elsevier Science, 2011), The Morgan Kaufmann Series in Data Management Systems, ISBN 9780123814807, https://books.google.co.ma/books?id=pQws07tdpjoC [Google Scholar]
  17. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A.I. Verkamo et al., Fast discovery of association rules. (AAAI/MIT Press Menlo Park, CA, 1996), Vol. 12, pp. 307–328 [Google Scholar]
  18. J. Han, J. Pei, Y. Yin, Mining Frequent Patterns without Candidate Generation (Association for Computing Machinery, New York, NY, USA, 2000), Vol. 29, p. 1–12, ISSN 0163-5808 [Google Scholar]
  19. S. Singh, A. Yassine, Mining Energy Consumption Behavior Patterns for Households in Smart Grid (2019), Vol. 7, pp. 404–419 [Google Scholar]
  20. S. Osama, M. Alfonse, A.B. M. Salem, Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data (2019), Vol. 3, ISSN 2504-2289, https://www.mdpi.com/2504-2289/3/2/20 [Google Scholar]

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