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Application of Extended Kernel Recursive Least Squares Method based on Unscented Kalman Filter in Short-term Photovoltaic Power Prediction

For photovoltaic power forecasting, based on extended kernel recursive least squares (Ex-KRLS) and unscented Kalman filter (UKF), an improved extended kernel recursive least squares method based on UKF (UKF-KRLS) is proposed in this paper. By adopting the idea of double alternating learning, this method complements the advantages of Ex-KRLS and UKF, which can better model the strongly nonlinear system and describe the internal characteristics of the prediction model more completely. In addition, the tracking performance of the prediction model can be greatly improved. To verify its hypothesis, this paper applies the UKF-KRLS method to the short-time interval power forecasting experiment of PV, and compares it with other prediction methods. Finally, it shows that UKF-KRLS is more reliable, which proves its practicability.