The comparison indicates that the MGA-ELMAN model without error
correction exhibits a certain model bias, with overall predictions being
lower than the actual values. The cascaded error correction model
proposed in this paper can mitigate the model bias inherent in the
preliminary prediction model, significantly enhancing the accuracy of
the predictions.
Conclusion: Accurate prediction of PV output enables the power
dispatch department to timely adjust the scheduling plan, significantly
enhancing the acceptance of PV grid connection and the stability and
security of the power system. In this paper, an error-corrected PV
output prediction model using a serial MGA-Elman neural network is
established, based on the PV output data from actual PV power stations.
The effectiveness of the serial error-corrected prediction model is
verified through comparison with the MGA-Elman error-corrected
prediction model, thereby providing a reference for predicting PV
output.