Abstract
Abstract Analysing the Output Power of a Solar Photo-voltaic System at the design stage and at the same time predicting the performance of solar PV System under different weather condition is a primary work i.e. to be carried out before any installation. Due to large penetration of solar Photovoltaic system into the traditional grid and increase in the construction of smart grid, now it is required to inject a very clean and economic power into the grid so that grid disturbance can be avoided. The level of solar Power that can be generated by a solar photovoltaic system depends upon the environment in which it is operated and two other important factor like the amount of solar insolation and temperature. As these two factors are intermittent in nature hence forecasting the output of solar photovoltaic system is the most difficult work. In this paper a comparative analysis of different solar photovoltaic forecasting method were presented. A MATLAB Simulink model based on Real time data which were collected from Odisha (20.9517∘N, 85.0985∘E), India. were used in the model for forecasting performance of solar photovoltaic system.
Highlights
Analysing the Output Power of a Solar Photovoltaic System at the design stage and at the same time predicting the performance of solar PV System under different weather condition is a primary work i.e. to be carried out before any installation
The level of solar Power that can be generated by a solar photovoltaic system depends upon the environment in which it is operated and two other important factor like the amount of solar insolation and temperature
A MATLAB Simulink model based on Real time data which were collected from Odisha (20.9517∘N, 85.0985∘E), India. were used in the model for forecasting performance of solar photovoltaic system
Summary
Loni j. et al [69] in their paper cloud advection forecasting has demonstrated about the method of forecasting using estimated cloud motion vector. Et al [69] in their paper cloud advection forecasting has demonstrated about the method of forecasting using estimated cloud motion vector. They have collected the data from roof top PV system. The method applied here is based on training on the recent measurement history and motion on “upwind” and “down wind” is assumed static. Skewness represent the asymmetry present in the system probability distribution function. Where γ represent the skewness index, e represents the error present in actual and forecasted result. Μe and σe represents the mean and standard deviation present in the forecasted value respectively. Where μ4 represents the 4th moment of mean and σ represents the standard the standard deviation of forecasted error
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