Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7488
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, Volume 3, Issue 1, Pages 83-119
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: 1751-2549 (print)
1756-2201 (online)
DOI: 10.3763/aber.2009.0304
Rights: © 2009 Earthscan
Type: Article
Appears in Collections:Άρθρα/Articles

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