Abstract
This paper presents a hybrid approach based on soft computing techniques in order to estimate ambient temperature for those places where such datum is not available. Indeed, we combine the Back-Propagation (BP) algorithm and the Simple Genetic Algorithm (GA) in order to effectively train neural networks in such a way that the BP algorithm initialises a few individuals of the GA’s population. Experiments have been performed over all the available Italian places and results have shown a remarkable improvement in accuracy compared to the single and traditional methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arbib, M.A.: The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)
Atlante Italiano della Radiazione Solare, http://www.solaritaly.enea.it
Buvik, K., Hestnes, A.G.: Interdisciplinary approach to sustainable building. Experiences from working with a Norwegian demonstration building on retrofitting. Nordic Journal of Architectural Research 20(3) (2008)
Erbs, D.G., Klein, S.A., Beckman, W.A.: Estimation of degree-days and ambient temperature bin data from monthly-average temperatures. Ashrae Journal, 60–65 (1983)
Farag, W.A., Quintana, V.H., Lambert-Torres, G.: Genetic Algorithms and Back-Propagation: a Comparative Study. In: Canadian Conference on Electrical and Computer Engineering Proceedings of the 1998 llth Canadian Conference on Electrical and Computer Engineering, CCECE. Part 1, pp. 93–96 (1998)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)
Haykin, S.: Neural Networks, a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey (1999)
Hestnes, A.G.: Integrated design processes - a must for low energy buildings. In: The Future of Civil Engineering (2008)
Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1975)
Jiang, Y.: Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models. Energy Policy 36, 3833–3837 (2008)
Khan, A.U., Bandopadhyaya, T.K., Sharma, S.: Genetic Algorithm Based Backpropagation Neural Network Performs better than Backpropagation neural Network in Stock Rates Prediction. Intl. Journal of Computer Science and Network Security 8(7), 162–166 (2008)
Klein, S.A., Beckman, W.A., Mitchell, J.W., Duffie, J.A., Duffie, N.A., Freeman, T.L., Mitchell, J.C., Braun, J.E., Evans, B.L., Kummer, J.P., Urban, R.E., Fiksel, A., Thornton, J.W., Blair, N.J., Williams, P.M., Bradley, D.E. (eds.): TRNSYS, a Transient System Simulation Program. Solar Energy Lab, Univ. of Wisconsin-Madison (2000)
Kosko, B.: Neural networks and fuzzy systems. Prentice-Hall, Englewood Cliffs (1992)
Krishnaiah, T., Srinivasa Rao, S., Madhumurthy, K., Reddy, K.S.: Neural Network Approach for Modelling Global Solar Radiation. Journal of Applied Sciences Research 3(10), 1105–1111 (2007)
Lu, C., Shi, B.: Hybrid Back-Propagation/Genetic Algorithm for Feedforward Neural Networks. In: ICSP 2000 (2000)
McInerney, M., Dhawan, A.P.: Use of Genetic Algorithms with Back Propagation in Training of Feed-Forward Neural Networks. In: IEEE International Conference on Neural Networks, pp. 203–208 (1993)
Mitra, A.K., Nath, S.: Forecasting maximum temperatures via fuzzy nearest neighbour model over Delhi. Appl. Comput. Math. 6(2), 288–294 (2007)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neur. Netw. 1(1), 4–27 (1990)
Nikravesh, M., Farell, A.E., Stanford, T.G.: Model identification of non linear time variant processes via artificial neural network. Computes and Chemical Engineering 20(11), 1277–1290 (1996)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by backpropagating errors. Nature 323, 533–536 (1986)
Sadeq, A.M., Wahdan, A.-M.A., Mahdi, H.M.K.: Genetic Algorithms and its use with back-propagation network. AIN Shams University Scientific Bullettin 35(3), 337–348 (2000)
Sen, Z.: Fuzzy algorithm for estimation irradiation from sunshine duration. Solar Energy 63(1), 39–49 (1998)
Soares, J., Oliveira, A.P., Zlata Boznar, M., Mlakar, P., Escobedo, J.F., Machado, A.J.: Modeling hourly diffuse solar-radiation in the city of So Paulo using a neural-network technique. Applied energy 79(2), 201–214 (2004)
Sodoudi, S.: Estimation of temperature, precipitation and evaporation with Neuro-Fuzzy method. In: Workshop of statistical downscaling, Oslo (2005)
Tatli, H., Sen, Z.: A new fuzzy modeling approach for predicting the maximum daily temperature from a time series. Journal of Engineering and Environmental Science 23, 173–180 (1999)
Zhang, M., Ciesielski, V.: Using Back Propagation Algorithm and Genetic Algorithms to Train and Refine Neural Networks for Object Detection. In: Computer Science Postgraduate Student Conference, Melbourne (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ceravolo, F., De Felice, M., Pizzuti, S. (2009). Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Ambient Temperature Modeling in Italy. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-01129-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01128-3
Online ISBN: 978-3-642-01129-0
eBook Packages: Computer ScienceComputer Science (R0)