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Theses

Commande prédictive hybride et apprentissage pour la synthèse de contrôleurs logiques dans un bâtiment.

Abstract : An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1.In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2.In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.
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Duc Minh Khang Le. Commande prédictive hybride et apprentissage pour la synthèse de contrôleurs logiques dans un bâtiment.. Autre. CentraleSupélec, 2016. Français. ⟨NNT : 2016CSUP0001⟩. ⟨tel-01646813⟩

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