Neural models for ambient temperature modelling

Abstract

In this work we show how to model ambient temperature through neural models. In particular we tried feed forward and fully recurrent architectures, trained with the back-propagation and evolutionary algorithms, to estimate the monthly average temperature and compared the results to the nearest neighbor approach. Therefore, the best neural model has been tested to get hourly estimations. We compared the outcomes to a well known tool which doesn't have such an estimation capability and results show that the proposed approach clearly outperforms the traditional ones.