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.

中文翻译：

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利用光伏电池板的实时退化计算提高太阳能功率估算的准确性

包括电力交易和规划在内的许多应用都需要实时准确估计太阳能。由于太阳能电池板的功率输出随着时间的推移而降低，因此如果没有退化参数，它的实时估计是困难的。在所提出的方法中，性能比方面的退化影响与其他气象参数结合在一起。使用基于聚类的技术实时计算退化，无需现场物理检查。最初，使用具有气象参数的支持向量回归 (SVR) 模型估计功率。估计与退化率同步进一步微调。该模型根据从太阳能发电厂获取的真实数据（气象参数和太阳能）进行了验证。细化后，估计结果显示在统计措施方面有显着改善。现在，根据决定系数 R 的估计精度²为 92%，误差指标归一化均方根误差 (NMRSE)、平均绝对百分比误差 (MAPE)、均方根误差 (RMSE) 分别为 7.13、5.92 和 14.54。