ECA-TCN PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON FEATURE COMBINATION
To effectively extract valuable temporal information from photovoltaic(PV)power data and further improve the accuracy of PV power prediction,we propose an efficient channel attention mechanism(ECA)-temporal convolutional network(TCN)prediction model based on multi-factor fusion.Firstly,the maximum information coefficient(MIC)is utilized to extract relevant features of PV power.Secondly,a polynomial feature derivation method is employed to combine the features of various factors,generating high-dimensional features and facilitating feature combination.Then,the ECA module,which adaptively selects the size of one-dimensional convolutional kernels,is combined with TCN to construct the ECA-TCN prediction model,which effectively captures the temporal characteristics of PV power data.Finally,multiple models are compared through experimental evaluation.The results demonstrate that the proposed feature combination method efficiently selects PV power data features and enhances their discriminative ability.The ECA-TCN prediction model with feature combination achieves a root mean square error(RMSE)of 0.0828 kW.Compared to LSTM,LSTM-TCN,and ECA-LSTM,the RMSE of the ECA-TCN was reduces by 0.29,0.23,and 0.13 percentage points,respectively,and exhibits the best fitting performance with an R2 value of 90.74% .This model effectively improves the accuracy of PV power prediction while maintaining a high fitting performance.
PV powerforecastingtemporal convolutional networkmax information coefficientefficient channel attention
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