It is well known that the knowledge of solar radiation represents a key for managing photovoltaic (PV) plants. In a smart grid scenario to predict the energy production can be considered a milestone. However, the unsteadiness of the weather phenomena makes the prediction of the energy produced by the solar radiation conversion process a difficult task. Starting from this considerations, the use of the data collected in the past represents only the first step in order to evaluate the variability both in a daily and seasonal fashion. In order to have a stronger dataset a multi-year observation is mandatory. In his paper, several autoregressive models are challenged on a two-year ground global horizontal radiation dataset measured in Milan,and the results are compared with those of simple predictor.

Statistical models approach for solar radiation prediction

CRISTALDI, LOREDANA;FAIFER, MARCO
2013-01-01

Abstract

It is well known that the knowledge of solar radiation represents a key for managing photovoltaic (PV) plants. In a smart grid scenario to predict the energy production can be considered a milestone. However, the unsteadiness of the weather phenomena makes the prediction of the energy produced by the solar radiation conversion process a difficult task. Starting from this considerations, the use of the data collected in the past represents only the first step in order to evaluate the variability both in a daily and seasonal fashion. In order to have a stronger dataset a multi-year observation is mandatory. In his paper, several autoregressive models are challenged on a two-year ground global horizontal radiation dataset measured in Milan,and the results are compared with those of simple predictor.
2013
2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
9781467346214
9781467346238
ELETTRICI
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/739968
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