Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1551485 | Solar Energy | 2010 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Ilaria Bertini, Francesco Ceravolo, Marco Citterio, Matteo De Felice, Biagio Di Pietra, Francesca Margiotta, Stefano Pizzuti, Giovanni Puglisi,