Open Access
Issue
ITM Web Conf.
Volume 69, 2024
International Conference on Mobility, Artificial Intelligence and Health (MAIH2024)
Article Number 01013
Number of page(s) 6
Section Artificial Intelligence
DOI https://doi.org/10.1051/itmconf/20246901013
Published online 13 December 2024
  1. J. P. Tricoire, «Why Buildings Are the Foundation of an Energy-Efficient Future», in World Economic Forum, 2021. [Google Scholar]
  2. L. Belussi et al., «A review of performance of zero energy buildings and energy efficiency solutions», J Build Eng, vol. 25, no 100772, 2019. [Google Scholar]
  3. A. Bagnasco, S. Massucco, M. Saviozzi, F. Silvestro, et A. Vinci, «Design and validation of a detailed building thermal model considering occupancy and temperature sensors», in 2018 IEEE 4th International Forum on Research and Technology for Society and Industry, RTSI: IEEE, 2018, p. 1–6. [Google Scholar]
  4. A. C. Menezes, A. Cripps, D. Bouchlaghem, et R. Buswell, «Predicted vs. actual energy performance of non-domestic buildings: Using postoccupancy evaluation data to reduce the performance gap», Applied energy, vol. 97, p. 355–364, 2012. [CrossRef] [Google Scholar]
  5. B. Dong, Z. Li, et G. Mcfadden, «An investigation on energy-related occupancy behavior for low-income residential buildings», Science and Technology for the Built Environment, vol. 21, no 6, p. 892–901, 2015, DOI: 10.1080/23744731.2015.1040321. [CrossRef] [Google Scholar]
  6. S. Hadri, M. Najib, M. Bakhouya, Y. Fakhri, et M. El Arroussi, «Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings», Energies, vol. 14, no 18, p. 5831, 2021. [CrossRef] [Google Scholar]
  7. O. A. Kabbaj, L.-M. Péan, J.-B. Masson, B. Marhic, et L. Delahoche, «Occupancy states forecasting with a hidden Markov model for incomplete data, exploiting daily periodicity», Energy and Buildings, vol. 287, p. 112985, mai 2023, DOI: 10.1016/j.enbuild.2023.112985. [CrossRef] [Google Scholar]
  8. S. Mahjoub, S. Labdai, L. Chrifi-Alaoui, B. Marhic, et L. Delahoche, «Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network», Energies, vol. 16, no 4, Art. no 4, janv. 2023, DOI: 10.3390/en16041641. [Google Scholar]
  9. S. Arvidsson, M. Gullstrand, B. Sirmacek, et M. Riveiro, «Sensor fusion and convolutional neural networks for indoor occupancy prediction using multiple low-cost low-resolution heat sensor data», Sensors, vol. 21, no 4, p. 1036, 2021. [CrossRef] [Google Scholar]
  10. A. N. Sayed, Y. Himeur, et F. Bensaali, «From time-series to 2d images for building occupancy prediction using deep transfer learning», Engineering Applications of Artificial Intelligence, vol. 119, p. 105786, 2023. [CrossRef] [Google Scholar]
  11. K. Sun, I. Qaisar, M. A. Khan, T. Xing, et Q. Zhao, «Building occupancy number prediction: A Transformer approach», Building and Environment, vol. 244, p. 110807, oct. 2023, DOI: 10.1016/j.buildenv.2023.110807. [CrossRef] [Google Scholar]
  12. M. K. Diarra, A. Maniar, J.-B. Masson, B. Marhic, et L. Delahoche, «Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context», Sensors, vol. 23, no 23, Art. no 23, janv. 2023, DOI: 10.3390/s23239603. [CrossRef] [Google Scholar]
  13. D. Calì, P. Matthes, K. Huchtemann, R. Streblow, et D. Müller, «CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings», Building and Environment, vol. 86, p. 39–49, avr. 2015, DOI: 10.1016/j.buildenv.2014.12.011. [CrossRef] [Google Scholar]
  14. Z. Yang et B. Becerik-Gerber, «Modeling personalized occupancy profiles for representing long term patterns by using ambient context», Building and Environment, vol. 78, p. 23–35, août 2014, DOI: 10.1016/j.buildenv.2014.04.003. [CrossRef] [Google Scholar]
  15. T. Ahmad et H. Chen, «Deep learning for multi-scale smart energy forecasting», Energy, vol. 175, p. 98–112, mai 2019, DOI: 10.1016/j.energy.2019.03.080. [CrossRef] [Google Scholar]
  16. S. Hochreiter et J. Schmidhuber, «Long shortterm memory», Neural computation, vol. 9, no 8, p. 1735–1780, 1997. [CrossRef] [Google Scholar]
  17. T. B. Brown et al., «Language Models are Few-Shot Learners», 22 juillet 2020, arXiv: arXiv:2005.14165. DOI: 10.48550/arXiv.2005.14165. [Google Scholar]
  18. S. Makridakis, E. Spiliotis, et V. Assimakopoulos, «Statistical and Machine Learning forecasting methods: Concerns and ways forward», PLOS ONE, vol. 13, no 3, p. e0194889, mars 2018, DOI: 10.1371/journal.pone.0194889. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.