A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control

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...

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Bibliographic Details
Published in:Energies (Basel) 2013-02, Vol.6 (2), p.733-747
Main Authors: Bracale, Antonio, Caramia, Pierluigi, Carpinelli, Guido, Di Fazio, Anna, Ferruzzi, Gabriella
Format: Article
Language:English
Subjects:
Alternative energy sources
autoregressive models
Bayesian inference
clearness index
Energy resources
Forecasting
Humidity
Methods
Monte Carlo simulation
Neural networks
photovoltaic generation
probability density functions
Radiation
Renewable resources
smart grid
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en6020733