# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 1 Introduction
Dropout is a regularization method in which input and recurrent connections to the LSTM units are probabilistically excluded from activation and weight updates when training a network [15,16]. GRU is a type of RNN that, in certain cases, has advantages over LSTM. GRU uses less memory and is faster than LSTM, although LSTM is more accurate when using datasets with longer sequences [17, 18, 19]. In this study, we intend to answer the question of which deep machine learning methods for time series data input can predict day-ahead wind power generation with the smallest error. To investigate the forecast accuracy of day-ahead for wind turbines measured with a performance evaluation index (i.e., MAPE), we developed a feature-based learning model for wind power forecasting and trained TCNs [20, 21, 22, 23] to learn meteorological features and identify the output class of power generation. We applied a multilayer neural convolutional architecture with gradient descent algorithms to minimize the estimation model error. Four major types of sequence-to-sequence DLN models for wind power forecasting were compared to assess model performance. The experimental results demonstrated that the TCN outperforms canonical recurrent networks, LSTMs, RNNs, and GRUs across a diverse range of experiments and datasets. Thus, the TCN provides an effective means of accurately predicting power generation under varying climate conditions. In summary, the primary contributions of this study are as follows:  
* The optimal parameters of the models were investigated using evolutionary algorithms (EAs) in order to minimize convergence loss in the learning process. * Four crucial architecture parameters for developed wind power prediction models were analysed, incorporating the differential evolution (DE) algorithm [16, 17, 18] in the learning process of the TCN model, namely, (i) number of filters, (ii) activation function, (iii) optimizer, and (iv) dilatation coefficient, in order to determine the initial model architecture for model training, according to the natural feature of TCN. * In our experiment, the prediction error of the TCN model for wind power prediction decreased most steadily among the four models, followed by LSTM, GRU, and RNN. * With an increasing amount of historical data, the prediction error (MAPE) of the TCN-based model decreased significantly; the 72 h forecast error of the 1-week, 1-month, and 1-year training datasets was 66.43%, 10.93%, and 5.13%, respectively. * Compared with LSTM, GRU, and RNN models, the TCN model created long effective memory in the deep learning framework and exhibited a lower forecast error to predict 24-, 48-, and 72-h ahead of wind power generation, which is more suitable for sequence modeling based on sequence-to-sequence applications. The remainder of this article is organized as follows. Section 2 provides a review of other relevant studies in this field, and Section 3 introduces the proposed TCN-based model for wind power forecasting. The results and performance analysis are presented in Section 4. Finally, Section 5 provides the concluding remarks.