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Hybrid approach of optimization for energy management in buildings

Authors: Oliveira, Grégory de;

Hybrid approach of optimization for energy management in buildings

Abstract

This PhD focuses on the global energy management based on energetical flux models ofbuildings. The objective of the energy management system is to help the inhabitants to manage theirbuilding, by scheduling the consumption/production of the different appliances, taking into accountenergy costs, availability et inhabitants' preferences. The PhD objective is to propose a resolution approach with several solvers inspired by the multi agent systems.A multi-phasis service representing several phasis has been developped. Each phasis is defined by its own consumption level. This type of service presents a better precision than singlephasis approaches to model some appliances. A new optimization approach has been developped. Itcombines different solvers embedded into software agents. The results is an hybrid approach forthe optimization based on the multi-agent system, using MILP algorithms and meta-heuristics.; Cette thèse concerne à la gestion globale à base de modèle des flux énergétiques dans le bâtiment. L'objectif des systèmes de gestion de l'énergie proposés est d'aider les occupants à gérer leur système bâtiment en planifiant la consommation/production des différents équipements présents en fonction des tarifs, de la disponibilité de l'énergie et des usages de l'occupant. L'objectif de cette thèse est de proposer une approche de résolution multi-solveurs s'appuyant sur le paradigme multi-agent.Pour y parvenir, nous avons développé, dans un premier temps, un service multi-phase, qui représente plusieurs phases flexibles avec des niveaux de consommation différents, offrant un niveau de précision supérieure pour des équipements comme une machine à laver. Dans un second temps, notre travail a consisté à proposer une nouvelle approche d'optimisation combinant différents solveurs embarqués dans des agents logiciels. Le résultat est une approche d'optimisation hybride à base d'agents, s'appuyant sur des algorithmes PLNE et des méta-heuristiques.

Country
France
Keywords

[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC], Ambient Intelligence, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Energy management, [SPI.OTHER] Engineering Sciences/Other, Intelligence ambiante dans l'habitat, Programmation linéaire, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, Linear programming, Multi agents, [ INFO.INFO-AU ] Computer Science [cs]/Automatic Control Engineering, [ INFO.INFO-MA ] Computer Science [cs]/Multiagent Systems [cs.MA], [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Gestion des flux énergétiques, [ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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