DocumentCode :
2526964
Title :
Neural models for ambient temperature modelling
Author :
Ceravolo, F. ; Di Pietra, Biagio ; Pizzuti, S. ; Puglisi, Giovanni
Author_Institution :
Energy New Technol. & Environ. Agency (ENEA), Rome
fYear :
2008
fDate :
14-16 July 2008
Firstpage :
60
Lastpage :
63
Abstract :
In this work we show how to model ambient temperature through neural models. In particular we tried feed forward and fully recurrent architectures, trained with the back-propagation and evolutionary algorithms, to estimate the monthly average temperature and compared the results to the nearest neighbor approach. Therefore, the best neural model has been tested to get hourly estimations. We compared the outcomes to a well known tool which doesn´t have such an estimation capability and results show that the proposed approach clearly outperforms the traditional ones.
Keywords :
backpropagation; environmental science computing; evolutionary computation; feedforward neural nets; recurrent neural nets; temperature measurement; ambient temperature modelling; backpropagation; evolutionary algorithm; feed forward neural net; fully recurrent neural net; Artificial neural networks; Computational modeling; Evolutionary computation; Feeds; Interpolation; Meteorology; Neural networks; Power system modeling; Spline; Temperature distribution; ambient temperature models; evolutionary algorithms; feed forward neural networks; fully recurrent neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications, 2008. CIMSA 2008. 2008 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-2305-7
Electronic_ISBN :
978-1-4244-2306-4
Type :
conf
DOI :
10.1109/CIMSA.2008.4595833
Filename :
4595833
Link To Document :
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