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

Didier Gossard (1) , Bérangère Lartigues (1) , Françoise Thellier (1)

1 PHASE - Laboratoire Physique de l'Homme Appliquée à Son Environnement

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 (c) 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 (c) 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.

Mots clés: Multi-objective optimization; Building envelope; Energy performance; Comfort degree; ANN; Genetic algorithm

Domaines: Eco-conception; Thermique [physics.class-ph]; Ingénierie assistée par ordinateur; Réseau de neurones [cs.NE]

Didier Gossard, Bérangère Lartigues, Françoise Thellier. Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network. Energy and Buildings, 2013, 67, pp.253-260. ⟨10.1016/j.enbuild.2013.08.026⟩. ⟨hal-02175513⟩