Plateforme IdO universelle alimentée par l’IA pour les bâtiments intelligents : renseignements fondés sur des données et axés sur la confidentialité
Aperçu du projet
Malgré l’émergence de diverses solutions intelligentes axées sur le secteur de la construction, des problèmes économiques et sociaux continuent à entraver la réalisation à grande échelle de bâtiments intelligents.
Ce projet a pour but de faire des technologies pour bâtiments intelligents un puissant catalyseur de l’amélioration de l’efficacité énergétique des bâtiments. On souhaite ainsi s'attaquer à plusieurs défis techniques relevés avec des partenaires industriels sur la base de recherches antérieures et d’expériences concrètes.
Renseignements clés
| Chercheur principal | Nizar Bouguila, professeur de l’Institut d’ingénierie des systèmes d’information de Concordia, Université Concordia |
Cochercheurs principaux |
Abdessamad Ben Hamza, professeur, Institut d’ingénierie des systèmes d’information de Concordia, Université de Concordia; Manar Amayri, professeur adjointe de l’Institut d’ingénierie des systèmes d’information de Concordia, Université Concordia |
Collaborateurs de recherche |
Ursula Eicker, titulaire de la Chaire d’excellence en recherche du Canada sur les collectivités et les villes intelligentes, durables et résilientes, Université Concordia; Maya Ezzeddine, responsable de la durabilité, bâtiments numériques, Schneider Electric; Youcef Chaoua, directeur principal, génie logiciel, Schneider Electric; Negar Ghourchian, vice-présidente, recherche et développement, Aerial Technologies |
| Partenaires non universitaires | Schneider Electric, Aerial |
| Mots-clés de la recherche | Efficacité des bâtiments, bâtiments intelligents, plateforme IdO, qualité des données, explicable, apprentissage automatique, apprentissage interactif, apprentissage par transfert, apprentissage fédéré |
| Budget | En espèces : 222 000 $ En nature : 382 000 $ |
Publications:
N. Mahamoodally, V. Tra, and M. Amayri, “Semi-supervised mixture of probabilistic principal component analyzers for modeling human behavior,” Energy and Buildings, vol. 346, p. 116145, Nov. 2025, doi: 10.1016/j.enbuild.2025.116145.
O. Abderrahim, J. Dridi, M. Amayri, and N. Bouguila, “Occupancy estimation and activity recognition in smart buildings using open set domain adaptation,” Journal of Building Engineering, vol. 112, p. 113784, Oct. 2025, doi: 10.1016/j.jobe.2025.113784.
O. Bregu and N. Bouguila, “Dirichlet compound negative multinomial mixture models and applications,” Adv Data Anal Classif, vol. 19, no. 3, pp. 795–830, Sep. 2025, doi: 10.1007/s11634-024-00598-2.
O. Bouhamed, M. Dissem, M. Amayri, and N. Bouguila, “Transformer-based deep probabilistic network for load forecasting,” Engineering Applications of Artificial Intelligence, vol. 152, p. 110781, Jul. 2025, doi: 10.1016/j.engappai.2025.110781.
A. Nfissi, W. Bouachir, N. Bouguila, and B. Mishara, “From unaltered raw waveform to emotion: Synergizing convolutional and gated recurrent networks for holistic speech emotion analysis,” Appl Intell, vol. 55, no. 8, p. 737, Jun. 2025, doi: 10.1007/s10489-025-06620-0.
S. B. Brahim, M. Amayri, and N. Bouguila, “One-day-ahead electricity load forecasting of non-residential buildings using a modified Transformer-BiLSTM adversarial domain adaptation forecaster,” Int. J. Dynam. Control, vol. 13, no. 5, p. 176, May 2025, doi: 10.1007/s40435-025-01701-x.
A. Baghbani, S. Rahmani, N. Bouguila, and Z. Patterson, “TMS-GNN: Traffic-aware Multistep Graph Neural Network for bus passenger flow prediction,” Transportation Research Part C: Emerging Technologies, vol. 174, p. 105107, May 2025, doi: 10.1016/j.trc.2025.105107.
S. Kallel, M. Amayri, and N. Bouguila, “Clustering and Interpretability of Residential Electricity Demand Profiles,” Sensors, vol. 25, no. 7, p. 2026, Mar. 2025, doi: 10.3390/s25072026.
J. Guo, W. Fan, M. Amayri, and N. Bouguila, “Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings,” Neural Networks, vol. 183, p. 106979, Mar. 2025, doi: 10.1016/j.neunet.2024.106979.
A. Zgaren, W. Bouachir, and N. Bouguila, “SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting,” J. Imaging, vol. 11, no. 2, p. 52, Feb. 2025, doi: 10.3390/jimaging11020052.
P. Koochemeshkian and N. Bouguila, “Flexible Distribution Approaches to Enhance Regression and Deep Topic Modelling Techniques,” Expert Systems, vol. 42, no. 2, p. e13789, Feb. 2025, doi: 10.1111/exsy.13789.
O. Sghaier, M. Amayri, and N. Bouguila, “Dirichlet and Liouville-based normality scores for deep anomaly detection using transformations: applications to images and beyond images,” Appl Intell, vol. 55, no. 1, p. 25, Jan. 2025, doi: 10.1007/s10489-024-05892-2.
J. Dridi, M. Amayri, and N. Bouguila, “Multi-Source Domain Adaptation Using Ambient Sensor Data,” Applied Artificial Intelligence, vol. 38, no. 1, p. 2429321, Dec. 2024, doi: 10.1080/08839514.2024.2429321.
O. Bouarada, M. Azam, M. Amayri, and N. Bouguila, “Hidden Markov models with multivariate bounded asymmetric student’s t-mixture model emissions,” Pattern Anal Applic, vol. 27, no. 4, p. 117, Dec. 2024, doi: 10.1007/s10044-024-01341-5.
S. M. Meshkani, S. Farazmand, N. Bouguila, and Z. Patterson, “Innovative On-Demand Transit for First-Mile Trips: A Cutting-Edge Approach,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2678, no. 11, pp. 122–136, Nov. 2024, doi: 10.1177/03611981241239970.
O. Sghaier, M. Amayri, and N. Bouguila, “Libby-Novick Beta-Liouville Distribution for Enhanced Anomaly Detection in Proportional Data,” ACM Trans. Intell. Syst. Technol., vol. 15, no. 5, pp. 1–26, Oct. 2024, doi: 10.1145/3675405.
S. Chouchene, M. Amayri, and N. Bouguila, “Sparse coding-based transfer learning for energy disaggregation,” Energy and Buildings, vol. 320, p. 114498, Oct. 2024, doi: 10.1016/j.enbuild.2024.114498.
A. Rebei, M. Amayri, and N. Bouguila, “Affinity-Driven Transfer Learning for Load Forecasting,” Sensors, vol. 24, no. 17, p. 5802, Sep. 2024, doi: 10.3390/s24175802.
S. Tabarsaii, M. Amayri, N. Bouguila, and U. Eicker, “Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation,” J Ambient Intell Human Comput, vol. 15, no. 9, pp. 3359–3378, Sep. 2024, doi: 10.1007/s12652-024-04814-x.
H. Al-Bazzaz, M. Azam, M. Amayri, and N. Bouguila, “Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management,” ACM Trans. Intell. Syst. Technol., vol. 15, no. 4, pp. 1–26, Aug. 2024, doi: 10.1145/3653980.
S. Paul, Z. Patterson, and N. Bouguila, “DualMLP: a two-stream fusion model for 3D point cloud classification,” Vis Comput, vol. 40, no. 8, pp. 5435–5449, Aug. 2024, doi: 10.1007/s00371-023-03114-3.
S. Samareh Abolhassani, A. Zandifar, N. Ghourchian, M. Amayri, N. Bouguila, and U. Eicker, “Occupant counting model development for urban building energy modeling using commercial off-the-shelf Wi-Fi sensing technology,” Building and Environment, vol. 258, p. 111548, Jun. 2024, doi: 10.1016/j.buildenv.2024.111548.
A. Nfissi, W. Bouachir, N. Bouguila, and B. Mishara, “Unveiling hidden factors: explainable AI for feature boosting in speech emotion recognition,” Appl Intell, vol. 54, no. 11–12, pp. 7046–7069, Jun. 2024, doi: 10.1007/s10489-024-05536-5.
J. Guo, M. Amayri, W. Fan, and N. Bouguila, “A scaled dirichlet-based predictive model for occupancy estimation in smart buildings,” Appl Intell, vol. 54, no. 11–12, pp. 6981–6996, Jun. 2024, doi: 10.1007/s10489-024-05543-6.
A. Algumaei, M. Azam, and N. Bouguila, “Novel approach for ECG separation using adaptive constrained IVABMGGMM,” Digital Signal Processing, vol. 149, p. 104476, Jun. 2024, doi: 10.1016/j.dsp.2024.104476.
M. K. Akbar, M. Amayri, N. Bouguila, B. Delinchant, and F. Wurtz, “Evaluation of regression models and Bayes-Ensemble Regressor technique for non-intrusive load monitoring,” Sustainable Energy, Grids and Networks, vol. 38, p. 101294, Jun. 2024, doi: 10.1016/j.segan.2024.101294.
M. Dissem, M. Amayri, and N. Bouguila, “Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder Design,” IEEE Internet Things J., vol. 11, no. 10, pp. 18059–18073, May 2024, doi: 10.1109/JIOT.2024.3360882.
P. Koochemeshkian, E. Ihou Koffi, and N. Bouguila, “Hidden Variable Models in Text Classification and Sentiment Analysis,” Electronics, vol. 13, no. 10, p. 1859, May 2024, doi: 10.3390/electronics13101859.
J. Dridi, M. Amayri, and N. Bouguila, “Unsupervised clustering-based domain adaptation for estimating occupancy and recognizing activities in smart buildings,” Journal of Building Engineering, vol. 85, p. 108741, May 2024, doi: 10.1016/j.jobe.2024.108741.
A. Rebei, M. Amayri, and N. Bouguila, “FSNet: A Hybrid Model for Seasonal Forecasting,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 8, no. 2, pp. 1167–1180, Apr. 2024, doi: 10.1109/TETCI.2023.3290050.
K. Maanicshah, M. Amayri, and N. Bouguila, “Novel mixture allocation models for topic learning,” Computational Intelligence, vol. 40, no. 2, p. e12641, Apr. 2024, doi: 10.1111/coin.12641.
W. Fan, L. Yang, and N. Bouguila, “Grouped Spherical Data Modeling Through Hierarchical Nonparametric Bayesian Models and Its Application to fMRI Data Analysis,” IEEE Trans. Neural Netw. Learning Syst., vol. 35, no. 4, pp. 5566–5576, Apr. 2024, doi: 10.1109/TNNLS.2022.3208202.
Publications acceptées dans des conférences nationales et internationales:
Z. Luo, W. Fan, M. Amayri, and N. Bouguila, “Dynamic Deep Clustering of High-Dimensional Directional Data via Hyperspherical Embeddings with Bayesian Nonparametric Mixtures,” in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, Toronto ON Canada: ACM, Jul. 2025, pp. 938–949. doi: 10.1145/3690624.3709230.
S. Hong, F. Najar, M. Amayri, and N. Bouguila, “Flexible Dirichlet Mixture Model for Multi-modal data Clustering,” in The International FLAIRS Conference Proceedings, May 2025. doi: 10.32473/flairs.38.1.138970.
F. Alkhawaja, M. Amayri, and N. Bouguila, “A hierarchical count data clustering based on Multinomial Nested Dirichlet Mixture using the Minorization-Maximization framework,” in The International FLAIRS Conference Proceedings, May 2024. doi: 10.32473/flairs.37.1.135263.
N. Bouguila, “Simultaneous count data feature selection and clustering using Multinomial Nested Dirichlet Mixture,” May 2024.
N. Bouguila, “Latent Beta-Liouville Probabilistic Modeling for Bursty Topic Discovery in Textual Data,” May 2024.
Un mélange d’enthousiasme et de responsabilité, La Presse, November 28, 2024.
Le transport collectif à la demande, un moyen efficace d’améliorer l’accessibilité en banlieue, The Conversation, October 24, 2024.
Using smart devices to schedule on-demand public transportation can save time and money, The Conversation, May 22, 2024.
But de la recherche
Intelligence artificielle (IA)
Grâce à l’intelligence artificielle (IA), les bâtiments intelligents peuvent économiser de l’énergie et améliorer l’expérience des occupantes et occupants. Les utilisations possibles de l’IA, dont la gestion de l’énergie, le contrôle personnalisé du climat, la maintenance prédictive et l’éclairage intelligent, favorisent une consommation d’énergie plus efficace.
Internet des objets (IdO)
L’un des principaux objectifs de ce projet vise la mise au point d’une plateforme de l’Internet des objets (IdO) offrant des services liés aux économies d’énergie. Par la création d’une plateforme IdO universelle alimentée par l’IA, on cherche à résoudre les problématiques liées à la diversité des capteurs, des technologies et des caractéristiques des bâtiments, la qualité des données recueillies, la complexité de l’étiquetage des données et les questions de confidentialité.
Partenaires non universitaires
Merci à nos partenaires non universitaires pour leur soutien et leur confiance.
