A Universal AI-Powered IoT Platform for Smart Buildings: Data-Driven Insights with Privacy Focus
Project overview
Despite the emergence of various intelligent solutions for the building sector, persistent economic issues and social concerns continue to pose challenges to the widespread deployment of smart building solutions.
The goal of this project is to develop intelligent building technology as a strong catalyst for increased energy efficiency in buildings. This research aims at tackling several technical challenges identified with industrial partners based on previous research and real-world experiences.
Key project details
| Principal investigator | Nizar Bouguila, professor, Concordia Institute for Information Systems Engineering, Concordia University |
Co-principal investigators |
Abdessamad Ben Hamza, professor, Concordia Institute for Information Systems Engineering, Concordia University; Manar Amayri, assistant professor, Concordia Institute for Information Systems Engineering, Concordia University |
Research collaborators |
Ursula Eicker, Canada Excellence Research Chair in Smart, Sustainable and Resilient Communities and Cities and director of the Next-Generation Cities Institute, Concordia University; Maya Ezzeddine, sustainability leader, Digital Buildings, Schneider Electric; Youcef Chaoua, senior software engineering manager, Schneider Electric; Negar Ghourchian, VP Research & Development, Aerial Technologies |
| Non-academic partners | Schneider Electric, Aerial |
| Research Keywords | building efficiency, smart buildings, iot platform, data quality, explainable, machine learning, interactive learning, transfer learning, federated learning |
| Budget | Cash: $222,000 In-Kind: $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.
Accepted publications in national and international conferences:
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.
Research focus
Artificial Intelligence (AI)
Smart buildings, with the help of Artificial Intelligence (AI), can save energy and make the experience better for people inside. To make more efficient energy consumption AI can be used in energy management, personalized climate control, predictive maintenance and smart lighting.
Internet of Things (IoT)
One of the leading focuses of this project is on developing an Internet of things (IoT) platform that allows offering energy-saving related services. By deploying AI and IoT technologies a universal AI-powered IoT platform will be developed that aims to overcome the challenges posed by: the diversity of sensors, technologies and buildings characteristics; quality of collected data; complexity of data labelling; and privacy issues.
Non-academic partners
Thank you to our non-academic partners for your support and trust.
Volt-Age is funded by a $123-million grant from the Canada First Research Excellence Fund.
