一种 TCN 的改进模型及其在短期光伏功率 区间预测的应用.

In order to improve the accuracy of PV power prediction, this paper proposed a new short-term PV power interval prediction model based on TCN. Firstly, the model used the soft threshold and attention mechanism of DRSN to modify the residual module of TCN so as to enhance its capability to extract useful features, and reduce the impact of redundant features. Then, it adopted the SSA to search the optimal hyper-parameters automatically, such as the convolutional kernel size and the number of TCN layers in the convolutional layer of the TCN, to overcome the drawback of the original TCN with insufficient receptive fields. Next, this paper applied the KDE to analyze the error of the point prediction results of the proposed improved TCN short-term PV power forecasting model to obtain its output interval. Finally, comparative simulation experiments show that the RMSE of the proposed SSA-DRSN-TCN model can reach 0.27,which is better than those of LSTM,GRU,CNN-LSTM and TCN, and the KDE method can accurately describe the PV power volatility intervals at 80%,90% and 95% confidence levels, the proposed model verifies the effectiveness in improving the performance of PV power prediction. [ABSTRACT FROM AUTHOR]