a bayesian method for short-term probabilistic forecasting of photovoltaic generation in smart grid operation and control

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ID: 147584
2013
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Abstract
A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.
Reference Key
ferruzzi2013energiesa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Gabriella Ferruzzi;Anna Rita Di Fazio;Guido Carpinelli;Pierluigi Caramia;Antonio Bracale
Journal acs combinatorial science
Year 2013
DOI
10.3390/en6020733
URL
http://www.mdpi.com/1996-1073/6/2/733
Keywords
probability density functions smart grid forecasting

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