Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/4405
Title: Artificial neural networks and genetic algorithms in energy applications in buildings
Authors: Kalogirou, Soteris A. 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Artificial neural networks;Genetic algorithms;Environmental parameters;Renewable energy systems;Naturally ventilated buildings;Energy consumption and conservation;HVAC systems
Issue Date: 2009
Source: Advances in Building Energy Research, 2009, vol. 3, no. 1, pp. 83-119
Volume: 3
Issue: 1
Start page: 83
End page: 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: https://hdl.handle.net/20.500.14279/4405
ISSN: 17562201
DOI: 10.3763/aber.2009.0304
Rights: © Taylor & Francis
Type: Article
Affiliation : Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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