This site is no longer actively updated; if you have a current subscription to Earthscan journals please go to https://www.ingentaconnect.com/register/institutional to register on the new site and gain access to your journal; if you are not a current subscriber go to sales@portland-services.com to subscribe, or to www.earthscan.co.uk/journals for more information.

Advances in Building Energy Research

Advances in Building Energy Research 3 (2009) 83–120

4

Artificial Neural Networks and Genetic Algorithms in Energy Applications in Buildings

Soteris A Kalogirou

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.

Keywords: artificial neural networks; genetic algorithms; environmental parameters; renewable energy systems; naturally ventilated buildings; energy consumption and conservation; HVAC systems

download full article