期刊
ENERGIES
卷 6, 期 2, 页码 733-747出版社
MDPI
DOI: 10.3390/en6020733
关键词
smart grid; photovoltaic generation; clearness index; forecasting; probability density functions; autoregressive models; Bayesian inference
资金
- Italian project PON Research and Competitiveness [Action II-PON 01_02864]
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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