Title :
Preprocessing Uncertain Photovoltaic Data
Author :
Miao Fan ; Vittal, Vijay ; Heydt, Gerald T. ; Ayyanar, Raja
Author_Institution :
Dept. of Electr., Comput., & Energy Eng., Arizona State Univ., Tempe, AZ, USA
Abstract :
This letter suggests a method to manage the uncertainty of photovoltaic (PV) data by removing the periodic effect of the annual position of the sun in the sky. The least squares method is applied to determine the low-frequency (annual) periodic component which is predictable in the system operation. This method can be applied to estimate the probabilistic characteristics of PV generation at various locations on the earth with differing insolation due to changing solar position.
Keywords :
least squares approximations; photovoltaic power systems; probability; PV generation; least square method; low-frequency periodic component; periodic effect; probabilistic characteristic estimation; solar position; system operation; uncertain photovoltaic data preprocessing; Photovoltaic systems; Probabilistic logic; Probability density function; Production; Sun; Uncertainty; Least squares method; photovoltaic (PV) generation; probability density function (pdf); renewable energy; uncertainty;
Journal_Title :
Sustainable Energy, IEEE Transactions on
DOI :
10.1109/TSTE.2013.2287992
