Portrait of Hanane Dagdougui

Hanane Dagdougui

Associate Academic Member
Full Professor, Polytechnique Montréal, Department of Mathematical and Industrial Engineering
Research Topics
Deep Learning
Distributed Systems
Optimization
LinkedIn

Biography

Hanane Dagdougui is a Full Professor at Polytechnique Montréal and Associate Academic Member of Mila - Quebec Artificial Intelligence Institute. She received the Ph.D. in Systems Engineering from the Faculty of Engineering of Genova and the Mines Paris-Tech in France, as part of an international joint program in 2011. Prior to joining the Polytechnique Montreal in 2017, she was a research assistant at the department of Informatics, Bioengineering, Robotics and System Engineering at the University of Genoa in 2013. From 2013 to 2016, she was an institutional researcher at the department of Electrical Engineering, ÉTS Montreal.

Her research interests are in the distributed optimization theory and applications of mathematical optimization. She is particularly interested in the applications of mathematical optimization and machine learning techniques to problems of smart grids, microgrids, and smart buildings. Her research interests include also the techno-economic modeling and planning of renewable energy-based systems, demand response and electric transportation.

Current Students

Mohamed Charara
Master's Research - Polytechnique Montréal
Principal supervisor :
Antoine Lesage-Landry
Fathipasandideh Fathipasandideh
PhD - Polytechnique Montréal
Github
Giorgi Gamkrelidze
Master's Research - Polytechnique Montréal
Co-supervisor :
Pierre-Luc Bacon
Arian Shah Kamrani
PhD - Polytechnique Montréal

Publications

Mixed-integer Second-Order Cone Programming for Multi-period Scheduling of Flexible AC Transmission System Devices
Mohamad Charara
Martin De Montigny
Nivine Abou Daher
Hanane Dagdougui
Antoine Lesage-Landry
2025-07-01
arXiv (published)
doi.org
arxiv.org
A Comparative Analysis of AI Models for Short-Term Solar Irradiance Forecasting
Saad Benbrahim
Abdelaziz Berrado
Hanane Dagdougui
Loubna Benabbou
2025-04-07
Proceedings of the International Conference on Industrial Engineering and Operations Management (published)
doi.org
Enhancing Hybrid Model for Photovoltaic Power Prediction: A Case Study of Morocco
Samira Abousaid
Abdelaziz Berrado
Hanane Dagdougui
Loubna Benabbou
2025-04-07
Proceedings of the International Conference on Industrial Engineering and Operations Management (published)
doi.org
Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection
Eslam G. Al-Sakkari
Ahmed Ragab
Mostafa Amer
Olumoye Ajao
Marzouk Benali
Daria C. Boffito
Hanane Dagdougui
Mouloud Amazouz
2025-03-01
Digital Chemical Engineering (published)
doi.org
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings
Vincent Taboga
Hanane Dagdougui
The surge in electricity use, coupled with the dependency on intermittent renewable energy sources, poses significant hurdles to effectively… (see more) managing power grids, particularly during times of peak demand. Demand Response programs and energy conservation measures are essential to operate energy grids while ensuring a responsible use of our resources This research combines distributed optimization using ADMM with Deep Learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. The resulting control algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.
2025-01-01
IEEE Transactions on Automation Science and Engineering (published)
doi.org
arxiv.org
A Distributed ADMM-Based Deep Learning Approach for Thermal Control in Multi-Zone Buildings Under Demand Response Events.
Vincent Taboga
Hanane Dagdougui
2025-01-01
IEEE Trans Autom. Sci. Eng. (published)
doi.org
arxiv.org
Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection
Eslam G. Al-Sakkari
Ahmed Ragab
Mostafa Amer
Olumoye Ajao
Marzouk Benali
Daria Camilla Boffito
Hanane Dagdougui
Mouloud Amazouz
2024-12-01
Digital Chemical Engineering (published)
doi.org
Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection
Eslam G. Al-Sakkari
Ahmed Ragab
Mostafa Amer
Olumoye Ajao
Marzouk Benali
Daria Camilla Boffito
Hanane Dagdougui
Mouloud Amazouz
2024-12-01
Digital Chemical Engineering (published)
doi.org
The Bifurcation Method: White-Box Observation Perturbation Attacks on Reinforcement Learning Agents on a Cyber Physical System
KIERNAN BRODA-MILIAN
Hanane Dagdougui
Ranwa Al Mallah
2024-09-20
International Conference on Machine Learning and Computing (published)
doi.org
Neural differential equations for temperature control in buildings under demand response programs
Vincent Taboga
Clement Gehring
Mathieu Le Cam
Hanane Dagdougui
Pierre-Luc Bacon
2024-08-01
Applied Energy (published)
doi.org
Neural differential equations for temperature control in buildings under demand response programs
Vincent Taboga
Clement Gehring
Mathieu Le Cam
Hanane Dagdougui
Pierre-Luc Bacon
2024-08-01
Applied Energy (published)
doi.org
Learn-To-Design: Reinforcement Learning-Assisted Chemical Process Optimization
Eslam G. Al-Sakkari
Ahmed Ragab
Mohamed Ali
Hanane Dagdougui
Daria C. Boffito
Mouloud Amazouz
2024-07-14
Systems and Control Transactions (published)
doi.org