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

摘要

The objective of this paper is to present a method to optimize the equivalent thermophysical properties of the external walls (thermal conductivity k_(wall) 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 Q_(TOT) and the summer comfort degree I_(sum). First, using a design of experiments, we have quantified and analyzed the impact of the variables k_(wall) and (ρc)_(wall) on the objectives Q_(TOT) and I_(sum). 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.