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Étude de l'aide à la décision par optimisation multicritère des programmes de réhabilitation énergétique séquentielle des bâtiments existants

Abstract : Under our latitudes, existing buildings energy consumptions, related to heating, cooling, ventilation, domestic hot water (DHW), lighting and other uses, are responsible for significant environmental burdens. Moreover, existing buildings annual replacement rate being lower than 1%, in most developed countries, existing stock retrofit represents a major lever to reach commitments on climate change and non-renewable energy consumption mitigation. However, the identification of optimal sustainable retrofit programs, including actions planning over a time period, is still a difficult task for professionals.This thesis aims at producing knowledge in order to contribute to decision support for efficient energy retrofit programs identification, through the application of different multi-criteria optimization techniques. The solutions (sequential building energy retrofit programs) are optimized both on their content and planning. The content refers to the combination of retrofit measures considered. These address holistically building envelopes (thermal insulation, windows replacement, window to wall ratios), and the replacement of equipment for ventilation, heating and DHW production. For each of these measures, various options are considered. The planning corresponds to the permutation of these measures, defining a time sequence for implementation. The solutions are evaluated on a multi-criteria and life cycle basis. The objective functions considered target environmental impacts evaluated using LCA (Life Cycle Assessment), some financial indicators and occupants' well-being through thermal comfort in summer. Life cycle assessment and life cycle cost models, using building dynamic thermal simulation for heating load and thermal comfort evaluation, are implemented to assess solutions performances.Considering the problem mathematical nature (multi-criteria, combinatorial, discrete variables, implicit non-linear objective functions), two suitable multi-criteria optimization techniques have been studied: multi-criteria genetic algorithm (NSGA-II) and dynamic programming. In the genetic approach, the modelling of each solution by a pair of chromosomes allowed to identify efficient sequential energy retrofit programs and analyse Pareto compromise surfaces, in terms of solutions features, performances and relationships in between criteria. Then, the representation of the problem on a sequential graph enabled us to apply dynamic programming, to compare both the genetic approximate solutions, and the results of some short- term approaches to the exact Pareto frontier. The search for exact solutions also been exploited to perform sensitivity analysis on different modelling parameters such as heating temperature setting, energy prices evolution or materials lifespan. Real life budget constraints have been incorporated to build a constrained multi-criteria genetic optimisation method, suitable to study retrofit strategies under financing plans. At the end, the genetic approach has been extended from building scale to stock scale and exact optimization has been used to characterize building types in terms of energy retrofit.The benefits of these methods have been illustrated on case studies. Knowledge has been produced in terms of multi criteria optimization methodology, applied to sequential energy retrofit, and understanding of building stocks evolution. These developments contribute to decision aiding; providing decision makers with efficient energy retrofit strategies and a description of the comprise surface, at the building or building stock scale, on a multi- criteria basis, over life cycle
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Submitted on : Thursday, September 12, 2013 - 10:32:12 AM
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Mathieu Rivallain. Étude de l'aide à la décision par optimisation multicritère des programmes de réhabilitation énergétique séquentielle des bâtiments existants. Autre. Université Paris-Est, 2013. Français. ⟨NNT : 2013PEST1038⟩. ⟨pastel-00861172⟩



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