A Combined Model for Ultra-short-term PV Forecasting based on SOM Clustering and ECA
In order to improve the prediction accuracy of PV output power under different weather types,a combined model of ultra-short-term PV prediction based on attention mechanism was proposed.First,the key meteorological factors closely related to PV power generation were selected by Pearson correlation coefficient analysis and standardized month by month,and then weighted summation was calculated to ob-tain the classification index sky condition factor(SCF)in order to reduce the dimensionality of input var-iables and eliminate the interference of season on weather classification and the coupling relationship be-tween numerous meteorological factors.Second,three types of weather types were classified by unsuper-vised clustering of SCFs through self-organizing map(SOM)neural network.Then,convolutional neural network(CNN)prediction models for each of the three weather types were constructed,and the efficient channel attention(ECA)module was introduced to adaptively assign relevant weights to each of the mul-tiple channels of feature information extracted by the CNN,so that the model focused on important feature information and the prediction accuracy of the model was improved.Finally,simulations were performed using historical measured data.The results show that the prediction accuracies of the proposed prediction model under three different weather types are improved by 1.006 1％,1.626 1％,1.610 4％,respec-tively,compared with the prediction model without the introduction of ECA module,which verifies its ef-fectiveness.
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