Dear Prof. Francesco,
thanks,
but I am interested in Mathematical Techniques for prediction PV power generation not in software.
What do you mean by "predict"? Which predictors do you have, and what timescales are you interested in?
If you are interested in forecasting future output given knowledge of past output, on the scale of minutes to hours, there are a number of papers comparing different forecasting methods for solar power output. Over short time horizons (e.g., less than 1 hour), persistence is difficult to beat. Time series methods (e.g., ARIMA) may have a slight advantage over persistence but not always, and it is hard to argue against the simplicity of a persistence forecast. Also, time series methods require some special treatment to handle sunrise correctly. Over longer time horizons, it is likely that you need a numerical weather simulation, because nature is not always a smoothly changing system.
For monthly predictions or annual predictions, a linear regression model will work with knowledge of cell temperature, direct and diffuse radiation.
http://www.scirp.org/journal/PaperDownload.aspx?paperID=32684
Dear Dr. Chauhan,
i don't know if it's too late to be helpful...
however i studied in the past years neural networks structures to predict the output of a PV plant up to 24 hours ahead, with data gathered from weather forecasts free websites...
i used a one stage method with a self learning structure, described in:
Online tuned neural networks for PV plant production forecasting, PVSC 2012
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6318197&searchWithin%3Dp_Authors%3A.QT.Ciabattoni%2C+L..QT.%26searchWithin%3Dp_Author_Ids%3A37571694000
and a two stage method (RBF neural network to forecast irradiance and PV model to obtain the output power):
Solar irradiation forecasting using RBF networks for PV systems with storage, ICIT 2012
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6210020&searchWithin%3Dp_Authors%3A.QT.Ciabattoni%2C+L..QT.%26searchWithin%3Dp_Author_Ids%3A37571694000
best regards
Lucio Ciabattoni
See the following links/refs:
solarelectricpower.orgPage on solarelectricpower.org
Inman, R. H., Pedro, H. T., & Coimbra, C. F. (2013). Solar forecasting methods for renewable energy integration. Progress in energy and combustion science, 39(6), 535-576.
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