| Title | Ambient temperature modelling with soft computing techniques |
|---|---|
| Publication Type | Articolo su Rivista peer-reviewed |
| Year of Publication | 2010 |
| Authors | Bertini, I., Ceravolo F., Citterio M., De Felice Matteo, Di Pietra B., Margiotta F., Pizzuti S., and Puglisi G. |
| Journal | Solar Energy |
| Volume | 84 |
| Pagination | 1264-1272 |
| ISSN | 0038092X |
| Keywords | accuracy 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 |
| 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. |
| Notes | cited By 8 |
| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953138188&doi=10.1016%2fj.solener.2010.04.003&partnerID=40&md5=ea49edd87a30a0ffabff263cf8861c15 |
| DOI | 10.1016/j.solener.2010.04.003 |
| Citation Key | Bertini20101264 |
