Accuracy Improvement of Solar Power Estimation Using Real-Time Degradation Computation of PV Panels

Abstract

Many applications including power trading and planning require the accurate estimation of solar power in real time. As the power output of the solar panels degrades over the time period, so its real-time estimation is tough without the degradation parameter. In the proposed method, the effect of degradation in terms of performance ratio is incorporated along with other meteorological parameters. The degradation is calculated in real time using the clustering-based technique without physical inspection on site. Initially, the power is estimated using Support Vector Regression (SVR) model with the meteorological parameters. The estimation is further fine-tuned in sync with the degradation rate. The model is validated on the real data (Meteorological parameters and Solar power) procured from the solar plant. After refinement, the estimation results show significant improvement in terms of statistical measures. Now, the estimation accuracy in terms of coefficient of determination R2 is 92% and the error metrics normalized root mean square error (NMRSE), mean absolute percentage error (MAPE), root mean square error (RMSE) are 7.13, 5.92 and 14.54, respectively.