Data di Pubblicazione:
2011
Citazione:
Illuminance prediction through SVM regression / F. Bellocchio, S. Ferrari, M. Lazzaroni, L. Cristaldi, M. Rossi, T. Poli, R. Paolini - In: EESMS 2011 : IEEE Workshop on environmental energy and structural monitoring systems : Università degli studi di Milano, september 28, 2011, Milan, Italy : proceedingsPiscataway : Institute of electrical and electronics engineers, 2011. - ISBN 9781457706103. - pp. 1-5 (( convegno IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS) tenutosi a Milano nel 2011 [10.1109/EESMS.2011.6067051].
Abstract:
In a scenario where renewable energies will play a foreground role, a reliable forecast of the energy production of such sources, like solar radiation, is a requirement for managing smart grids. However, the ability to predict the possibility to produce sustainable energy in different climatic conditions can be very useful for many other purposes (e.g., for Climate Sensitive Buildings). This is particularly true when working with climatic data that are, as a matter of fact, highly unsteady. Nevertheless, the use of data collected in the past can help to face the daily and seasonal variability. An algorithm for illuminance prediction based on Support Vector Regression (SVR) is here proposed and the results are presented and discussed.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Data models ; Kernel ; Optimization ; Predictive models ; Solar radiation ; Support vector machines ; Training.
Elenco autori:
F. Bellocchio, S. Ferrari, M. Lazzaroni, L. Cristaldi, M. Rossi, T. Poli, R. Paolini
Link alla scheda completa:
Titolo del libro:
EESMS 2011 : IEEE Workshop on environmental energy and structural monitoring systems : Università degli studi di Milano, september 28, 2011, Milan, Italy : proceedings