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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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