A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control
by
Antonio Bracale 1
, Pierluigi Caramia 1, Guido Carpinelli 2, Anna Rita Di Fazio 3,* and Gabriella Ferruzzi 4
Antonio Bracale 1, Pierluigi Caramia 1, Guido Carpinelli 2, Anna Rita Di Fazio 3,* and Gabriella Ferruzzi 4
1
Department for Technologies, University Parthenope of Napoli, Centro Direzionale di Napoli, Is. C4, 80143 Napoli, Italy
2
Department of Electrical Engineering and of Information Technologies, University Federico II of Napoli, via Claudio 21, 80125 Napoli, Italy
3
Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, via Di Biasio 43, 03042 Cassino, Italy
4
Department of Economical-Management Engineering, University Federico II of Napoli, Piazzale V. Tecchio 80, 80125 Napoli, Italy
*
Author to whom correspondence should be addressed.
Energies 2013, 6(2), 733-747; https://doi.org/10.3390/en6020733
Received: 28 December 2012 / Revised: 24 January 2013 / Accepted: 25 January 2013 / Published: 6 February 2013
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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.
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Keywords:
smart grid; photovoltaic generation; clearness index; forecasting; probability density functions; autoregressive models; Bayesian inference
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MDPI and ACS Style
Bracale, A.; Caramia, P.; Carpinelli, G.; Di Fazio, A.R.; Ferruzzi, G. A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control. Energies 2013, 6, 733-747.
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