Optimisation of thermal performances in livestock housing design solutions using genetic algorithms

Various design elements can affect the energy efficiency of buildings. Usually, parametric analysis does not take into account the interactive effects between the different features in terms of building energy use. Genetic algorithm (GA)-based optimisation approach relies on the evolutionary concept of natural selection to converge on an optimal solution and links together many parameters and several 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 optimisation tool to find suitable design solutions in terms of thermal performance. This process is applied to a simple sheepfold model located in Mediterranean climate for a medium-size extensive enterprise. The study analyses only passive design solutions.

[1]  Essia Znouda,et al.  Optimization of Mediterranean building design using genetic algorithms , 2007 .

[2]  Salvatore Carlucci,et al.  Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design , 2013 .

[3]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[4]  Joseph Andrew Clarke,et al.  Energy Simulation in Building Design , 1985 .

[5]  Christine M. Anderson-Cook.  Practical Genetic Algorithms (2nd ed.) , 2005 .

[6]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[9]  David Coley,et al.  Low-energy design: combining computer-based optimisation and human judgement , 2002 .

[10]  Moncef Krarti,et al.  Design optimization of energy efficient residential buildings in Tunisia , 2012 .

[11]  Anna Laura Pisello,et al.  A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity , 2012 .

[12]  Leslie K. Norford,et al.  A design optimization tool based on a genetic algorithm , 2002 .

[13]  M. Caroprese.  Sheep housing and welfare , 2008 .

[14]  Kristian Fabbri,et al.  Energy performance building evaluation in Mediterranean countries: Comparison between software simulations and operating rating simulation , 2008 .

[15]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[16]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[17]  Kalyanmoy Deb,et al.  Understanding Interactions among Genetic Algorithm Parameters , 1998, FOGA.