Abstract
The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for simulation, optimisation and controller synthesis.
In this paper we describe a work from this interdisciplinary area. We introduce our tool for the optimisation of parameterised solar thermal power plants, and report on the employment of genetic algorithms and neural networks for parameter synthesis. Experimental results show the applicability of our approach.
This work is based on the Fraunhofer ISE project “optisim”, which was funded by the German Ministry of Environment (project number FKZ 0325045).
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References
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Bishop, J.M., Bushnell, M.J., Usher, A., Westland, S.: Genetic optimisation of neural network architectures for colour recipe prediction. In: International Joint Conference on Neural Networks and Genetic Algorithms, pp. 719–725 (1993)
Goldberg, D.E., et al.: Genetic algorithms in search, optimization, and machine learning. Addison-wesley, Reading (1989)
Hancock, P., Smith, L.: GANNET: Genetic design of a neural net for face recognition. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 292–296. Springer, Heidelberg (1991)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America 79(8), 2554 (1982)
Liau, E., Schmitt-Landsiedel, D.: Automatic worst case pattern generation using neural networks & genetic algorithm for estimation of switching noise on power supply lines in cmos circuits. In: European Test Workshop, IEEE, pp. 105–110 (2003)
Lopez, R.: Flood: An open source neural networks C++ library. Universitat Politècnica de Catalunya, Barcelona (2008), http://www.cimne.com/flood
Mandic, D.P., Chambers, J.A.: Recurrent neural networks for prediction: Learning algorithms, architectures and stability. Wiley, Chichester (2001)
Maniezzo, V.: Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks 5(1) (1994)
Morin, G.: Techno-economic design optimization of solar thermal power plants. PhD thesis, Technische Universität Braunschweig (2010)
Schimann, W., Joost, M., Werner, R.: Application of genetic algorithms to the construction of topologies for multilayer perceptrons. In: International Joint Conference on Neural Networks and Genetic Algorithms, pp. 675–682 (1993)
Software Thermoflex: software developed and distributed by Thermoflow Inc. http://www.thermoflow.com/ .
Wall, M.: GAlib: A C++ library of genetic algorithm components (1996), http://lancet.mit.edu/ga/
Wittwer, C.: ColSim Simulation von Regelungssystemen in aktiven Solarthermischen Anlagen. PhD thesis, Universität Karlsruhe (1998)
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Richter, P., Ábrahám, E., Morin, G. (2011). Optimisation of Concentrating Solar Thermal Power Plants with Neural Networks. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_20
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DOI: https://doi.org/10.1007/978-3-642-20282-7_20
Publisher Name: Springer, Berlin, Heidelberg
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