Portrait de Hanane Dagdougui

Hanane Dagdougui

Membre académique associé
Professeure titulaire, Polytechnique Montréal, Département de mathématiques et de génie industriel
Sujets de recherche
Apprentissage profond
Optimisation
Systèmes distribués
LinkedIn

Biographie

Hanane Dagdougui est professeure titulaire à Polytechnique Montréal et membre académique associée à Mila - Institut québécois d'intelligence artificielle. Elle a obtenu le doctorat en ingénierie des systèmes de la Faculté d'ingénierie de Gênes et des Mines Paris-Tech en France, dans le cadre d'un programme international conjoint en 2011. Avant de rejoindre Polytechnique Montréal en 2017, elle a été assistante de recherche au département d'informatique, de bio-ingénierie, de robotique et d'ingénierie des systèmes de l'Université de Gênes en 2013. De 2013 à 2016, elle a été chercheuse institutionnelle au département de génie électrique de l'ÉTS Montréal.

Ses recherches portent sur la théorie de l'optimisation distribuée et les applications de l'optimisation mathématique. Elle s'intéresse particulièrement aux applications de l'optimisation mathématique et des techniques d'apprentissage automatique aux problèmes des réseaux intelligents, des micro-réseaux et des bâtiments intelligents. Ses recherches portent également sur la modélisation technico-économique et la planification des systèmes basés sur les énergies renouvelables, la réponse à la demande et le transport électrique.

Étudiants actuels

Fathipasandideh Fathipasandideh
Doctorat - Polytechnique
Giorgi Gamkrelidze
Maîtrise recherche - Polytechnique
Co-superviseur⋅e :
Pierre-Luc Bacon
Arian Shah Kamrani
Doctorat - Polytechnique

Publications

A Novel Sequential Framework for Transmission Network Expansion Planning: Benders Decomposition Preceding Semidefinite Programming
Elmira Fathipasandideh
Hussein Suprême
Hanane Dagdougui
Dalal Asber
The transmission network expansion planning (TNEP) problem is inherently complex because of its nonlinear and nonconvex nature, arising from… (voir plus) the inclusion of AC power flow constraints, discrete investment decisions, and multiple operating scenarios. These characteristics make the problem computationally challenging, particulary when scaling to larger systems with multistage planning horizons. Addressing this complexity requires advanced methodologies that balance the solution accuracy and computational efficiency. This paper presents a novel two-step framework for TNEP that first applies Benders decomposition to separate investment and operational decisions, followed by semidefinite linearization to reformulate the operational subproblems. The proposed approach enhances the solution quality by ensuring convexity in the subproblems and improves computational efficiency through decomposition. Numerical results for 6- , 10-, and 24-bus test systems demonstrate that the proposed method achieves superior performance compared to existing approaches in terms of solution accuracy and computational efficiency.
2025-07-03
2025 IEEE Kiel PowerTech (publié)
doi.org
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 (publié)
doi.org
arxiv.org
A Novel Sequential Framework for Transmission Network Expansion Planning: Benders Decomposition Preceding Semidefinite Programming
Elmira Fathipasandideh
Hussein Suprême
Hanane Dagdougui
Dalal Asber
The transmission network expansion planning (TNEP) problem is inherently complex because of its nonlinear and nonconvex nature, arising from… (voir plus) the inclusion of AC power flow constraints, discrete investment decisions, and multiple operating scenarios. These characteristics make the problem computationally challenging, particulary when scaling to larger systems with multistage planning horizons. Addressing this complexity requires advanced methodologies that balance the solution accuracy and computational efficiency. This paper presents a novel two-step framework for TNEP that first applies Benders decomposition to separate investment and operational decisions, followed by semidefinite linearization to reformulate the operational subproblems. The proposed approach enhances the solution quality by ensuring convexity in the subproblems and improves computational efficiency through decomposition. Numerical results for 6- , 10-, and 24-bus test systems demonstrate that the proposed method achieves superior performance compared to existing approaches in terms of solution accuracy and computational efficiency.
2025-06-29
2025 IEEE Kiel PowerTech (publié)
doi.org
A Novel Sequential Framework for Transmission Network Expansion Planning: Benders Decomposition Preceding Semidefinite Programming
Elmira Fathipasandideh
Hussein Suprême
Hanane Dagdougui
Dalal Asber
The transmission network expansion planning (TNEP) problem is inherently complex because of its nonlinear and nonconvex nature, arising from… (voir plus) the inclusion of AC power flow constraints, discrete investment decisions, and multiple operating scenarios. These characteristics make the problem computationally challenging, particulary when scaling to larger systems with multistage planning horizons. Addressing this complexity requires advanced methodologies that balance the solution accuracy and computational efficiency. This paper presents a novel two-step framework for TNEP that first applies Benders decomposition to separate investment and operational decisions, followed by semidefinite linearization to reformulate the operational subproblems. The proposed approach enhances the solution quality by ensuring convexity in the subproblems and improves computational efficiency through decomposition. Numerical results for 6- , 10-, and 24-bus test systems demonstrate that the proposed method achieves superior performance compared to existing approaches in terms of solution accuracy and computational efficiency.
2025-06-29
2025 IEEE Kiel PowerTech (publié)
doi.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 (publié)
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 (publié)
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 (publié)
doi.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. (publié)
doi.org
arxiv.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… (voir plus) 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 (publié)
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 (publié)
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 (publié)
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 (publié)
doi.org