Short-Term PV Cluster Power Prediction Based on Fuzzy C-Means and Itransformer-TCN

Accurate photovoltaic (PV) power prediction is extremely critical for the safe and stable operation of power systems. Aiming at the problems of inaccurate cluster delineation and difficulty in improving the short-term prediction accuracy in the current PV power cluster prediction, this paper proposes a short-term power prediction method for PV power clusters that integrates the fuzzy C-mean (FCM) and the improved Transformer-Temporal Convolutional Network (TCN). Initially, subsets are clustered using the FCM clustering algorithm. Subsequently, the ITransformer-TCN model is employed to leverage its dual feature extraction capabilities for modeling and predicting each subset. A PV power station in Guangxi Province is applied to test this proposed method c, and comparative analysis demonstrates that this method achieves an average reduction of Root Mean Square Error by 8.9% compared to other methods.