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|Title:||Artificial neural networks and genetic algorithms in energy applications in buildings||Authors:||Kalogirou, Soteris A.||Keywords:||Neural networks (Computer science);Genetic algorithms;Renewable energy resources;Air conditioning;Buildings||Category:||Environmental Engineering||Field:||Engineering and Technology||Issue Date:||2009||Publisher:||Taylor & Francis||Source:||Advances in Building Energy Research, 2009, vol. 3, no. 1, pp. 83-119||Journal:||Advances in Building Energy Research||Abstract:||The major objective of this chapter is to illustrate how artificial neural networks (ANNs) and genetic algorithms (GAs) may play an important role in modelling and prediction of the performance of various energy systems in buildings. The chapter initially presents artificial neural networks and genetic algorithms and outlines an understanding of how they operate by way of presenting a number of problems in the different disciplines of energy applications in buildings including environmental parameters, renewable energy systems, naturally ventilated buildings, energy consumption and conservation, and HVAC systems. The various applications are presented in a thematic rather than a chronological or any other order. Results presented in this chapter are testimony of the potential of artificial neural networks and genetic algorithms as design tools in many areas of energy applications in buildings||URI:||http://ktisis.cut.ac.cy/handle/10488/7488||ISSN:||1756-2201||DOI:||10.3763/aber.2009.0304||Rights:||© Taylor & Francis 2009||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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