Ambient temperature modelling with soft computing techniques

TitoloAmbient temperature modelling with soft computing techniques
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2010
AutoriBertini, I., Ceravolo F., Citterio M., De Felice Matteo, Di Pietra B., Margiotta F., Pizzuti S., and Puglisi G.
RivistaSolar Energy
Volume84
Paginazione1264-1272
ISSN0038092X
Parole chiaveaccuracy assessment, Ambient temperatures, Artificial Neural Network, Artificial neural networks, back propagation, Backpropagation algorithms, BP algorithm, Daily temperatures, estimation method, genetic algorithm, Genetic algorithms, Hybrid approach, Neural networks, numerical model, Simple genetic algorithm, Soft computing, Softcomputing techniques, software, Temperature distribution, Temperature estimation, Temperature modelling, temperature profile, Thermoanalysis
Astratto

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.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77953138188&doi=10.1016%2fj.solener.2010.04.003&partnerID=40&md5=ea49edd87a30a0ffabff263cf8861c15
DOI10.1016/j.solener.2010.04.003