Low Voltage Substation Photovoltaic Ultra Short Term Power Prediction Method Based on FCM-SENet-TCN

The existing methods for predicting photovoltaic power face problems such as excessive initial data redundancy and difficulty in extracting predictive features when facing distributed photovoltaics in low-voltage substations, resulting in insufficient prediction accuracy. Therefore, this article proposes a low-voltage photovoltaic ultra short term power prediction method based on FCM-SENet TCN. Firstly, the fuzzy C-means clustering algorithm (FCM) is used to fully explore multi-source meteorological environment data, clustering the initial dataset with different weather conditions to reduce initial data redundancy; Secondly, the Squeeze and Excitation Networks (SENet) will be integrated into the Temporal Convolutional Network (TCN) to efficiently extract complex features and improve prediction accuracy; Finally, the average absolute percentage error and root mean square error are used as evaluation indicators to assess the prediction results. The simulation results show that the proposed prediction method can fully utilize the initial meteorological data and make more accurate ultra short term power predictions based on the output characteristics of distributed photovoltaic generators in low-voltage substations.