Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network

The objective of this paper is to present a method to optimize the equivalent thermophysical properties of the external walls (thermal conductivity kwall and volumetric specific heat (ρc)wall) of a dwelling in order to improve its thermal efficiency. Classical optimization involves several dynamic yearly thermal simulations, which are commonly quite time consuming. To reduce the computational requirements, we have adopted a methodology that couples an artificial neural network and the genetic algorithm NSGA-II. This optimization technique has been applied to a dwelling for two French climates, Nancy (continental) and Nice (Mediterranean). We have chosen to characterize the energy performance of the dwelling with two criteria, which are the optimization targets: the annual energy consumption QTOT and the summer comfort degree Isum. First, using a design of experiments, we have quantified and analyzed the impact of the variables kwall and (ρc)wall on the objectives QTOT and Isum, depending on the climate. Then, the optimal Pareto fronts obtained from the optimization are presented and analyzed. The optimal solutions are compared to those from mono-objective optimization by using an aggregative method and a constraint problem in GenOpt. The comparison clearly shows the importance of performing multi-objective optimization.