In this paper we present a study on the feasibility of the prediction of the solar radiation on a location giving the meteorological measurement in surrounding locations on a mesoscale system scale. The data from four public stations run by the Lombardy regional agency for environmental protection (ARPA) have been used as dataset for training a neural network in order to predict with one-hour lag the global radiation in one station using the data from the other three stations. The results have been compared with other two models: the first makes use of only the data from the station to be predicted, while the second exploits all the available information considering all the four stations as input sources. The dataset has been formed using data from the ARPA stations in Milano, Crema, Osio sotto, e Cassano d'Adda, considering the years 2002-2007.
Multi-point solar prediction through feed-forward neural networks / S. Ferrari, C. Leani, V. Piuri - In: 2014 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems Proceedings[s.l] : IEEE, 2014. - ISBN 9781479949892. - pp. 23-27 (( convegno IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS) tenutosi a Napoli nel 2014.
|Titolo:||Multi-point solar prediction through feed-forward neural networks|
|Parole Chiave:||Radiation prediction; series; models|
|Settore Scientifico Disciplinare:||Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni|
|Data di pubblicazione:||2014|
|Enti collegati al convegno:||IEEE Italy Section|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/EESMS.2014.6923259|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|