Optimization of thermal performances in livestock housing design solutions using Genetic Algorithms

Various design elements can affect the energy efficiency of buildings. Usually, parametric analysis doesn’t take into account the interactive effects between the different features in terms of building energy use. Genetic Algorithm (GA) based optimization approach relies on the evolutionary concept of natural selection to converge on an optimal solution and links together many parameters and several solutions. Many studies underline the effectiveness of coupling GA-based optimization techniques with dynamic thermal models in order to analyze energy efficiency design solutions. This methodology is well documented in residential buildings but so far few tries were made to extend this process to livestock housing and service facilities. In this paper, Genetic Algorithms are applied as an optimization tool to find suitable design solutions in terms of thermal performance. Initially, the GA generates a random population of design configurations, which are then evaluated using a dynamic thermal model to assess energy performances. The results from the simulations are used by the GA to generate, with an objective-oriented stochastic operator, a new set of possible configurations defined by better energy performances. Thus the algorithm guides the search, generation by generation, towards low-energy design solutions. The combined process is applied to a simple sheepfold model located in Mediterranean climate for a medium-size extensive enterprise. The study analyzes only passive design solutions, since livestock facilities, especially those serving extensive farming which are prevalent in Mediterranean areas, need low energy input to achieve thermal and lighting comfort, when an appropriate design of the building envelope and windows is adopted.