Ambient temperature modelling with soft computing techniques

Ilaria Bertini, Francesco Ceravolo, Marco Citterio, Matteo De Felice, Biagio Di Pietra, Francesca Margiotta, Stefano Pizzuti, Giovanni Puglisi

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11 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1264 - 1272
Number of pages9
JournalSolar Energy
Issue number7
Publication statusPublished - Jul 2010
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

Cite this

Bertini, I., Ceravolo, F., Citterio, M., De Felice, M., Di Pietra, B., Margiotta, F., ... Puglisi, G. (2010). Ambient temperature modelling with soft computing techniques. Solar Energy, 84(7), 1264 - 1272.