Wind turbine output power forecasting based on temporal convolutional neural network and complete ensemble empirical mode decomposition with adaptive noise

Predicting the output power of the wind turbine accurately is an important means to ensure the stable operation of the wind power system. More and more deep learning methods are currently applied to the prediction of the output power, but few of them pay attention to the non-stationarity of the output power, which leads to a decrease in the prediction accuracy. In this study, a hybrid forecasting model that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and temporal convolutional neural network (TCN) is developed to predict the output power. First, the CEEMDAN method is used to decompose the original output power sequence into several sub-sequences of different frequencies. Then, the TCN model is used to predict each sub-sequence. Finally, the sum of the prediction results is used as the predicted value of the output power. Take a wind turbine in Shanghai as an example, the experimental results show that the CEEMDAN-TCN model has the highest prediction accuracy when compared with the current mainstream deep learning models. Compared with the single TCN, the mean square error (MSE) predicted by the CEEMDAN-TCN model is reduced by 17%, and the root mean square error (RMSE) is reduced by 11%.