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|Title:||Artificial neural networks and genetic algorithms for the modeling, simulation, and performance prediction of solar energy systems||Authors:||Kalogirou, Soteris A.||Keywords:||Artificial neural network;ANN;Solar energy systems;Genetic algorithms||Category:||Mechanical Engineering||Field:||Engineering and Technology||Issue Date:||1-Dec-2013||Publisher:||Springer Science+Business Media||Source:||Assessment and Simulation Tools for Sustainable Energy Systems, 2013, Pages 225-245||metadata.dc.doi:||10.1007/978-1-4471-5143-2_11||Abstract:||In this chapter, two of the most important artificial intelligence techniques are presented together with a variety of applications in solar energy systems. Artificial neural network (ANN) models represent a new method in system modeling and prediction. An ANN mimics mathematically the function of a human brain. They learn the relationship between the input parameters, usually collected from experiments, and the controlled and uncontrolled variables by studying previously recorded data. A genetic algorithm (GA) is a model of machine learning, which derives its behavior from a representation of the processes of evolution in nature. GAs can be used for multidimensional optimization problems in which the character string of the chromosome can be used to encode the values for the different parameters being optimized. The chapter outlines an understanding of how ANN and GA operate by way of presenting a number of problems in different solar energy systems applications, which include modeling and simulation of solar systems, prediction of the performance, and optimization of the design or operation of the systems. The systems presented include solar thermal and photovoltaic systems.||URI:||http://ktisis.cut.ac.cy/handle/10488/9697||ISBN:||978-1-4471-5142-5||Rights:||© Springer-Verlag London 2013.||Type:||Book Chapter|
|Appears in Collections:||Άρθρα/Articles|
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