Title of article
Ambient temperature modelling with soft computing techniques
Author/Authors
Ilaria Bertini a، نويسنده , , Francesco Ceravolo a، نويسنده , , Marco Citterio a، نويسنده , , Matteo De Felice a، نويسنده , , b، نويسنده , , Biagio Di Pietra a، نويسنده , , Francesca Margiotta a، نويسنده , , Stefano Pizzuti a، نويسنده , , *، نويسنده , , Giovanni Puglisi، نويسنده ,
Issue Information
ماهنامه با شماره پیاپی سال 2010
Pages
9
From page
1264
To page
1272
Abstract
This paper proposes a hybrid approach based on soft computing techniques in order to estimate monthly and daily ambient temperature.
Indeed, we combine the back-propagation (BP) algorithm and the simple Genetic Algorithm (GA) in order to effectively train
artificial neural networks (ANN) in such a way that the BP algorithm initialises a few individuals of the GA’s population. Experiments
concerned monthly temperature estimation of unknown places and daily temperature estimation for thermal load computation. Results
have shown remarkable improvements in accuracy compared to traditional methods.
2010 Elsevier Ltd. All rights reserved
Keywords
Soft computing , Temperature modelling , Artificial neural networks
Journal title
Solar Energy
Serial Year
2010
Journal title
Solar Energy
Record number
940372
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