# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 2 Literature Review
### DNNs for Processing Time-Series Data  
To address the issues involved in wind power forecasting, researchers have developed DNNs, which include the RNN, LSTM, GRU, and TCN and can be applied to address complex nonlinear relations between wind power output and climate data.  
RNNs can manage several types of sequence problems, including speech and text recognition, language-to-language translation, handwriting recognition, and sequence data analysis (i.e., forecasting). Generally, RNNs are the best candidate for sequence-to-sequence learning because their internal memory gates obtain outstanding results in natural language processing and other applications. However, RNNs have limited testing with wind time-series data, as well as long memory requirements. LSTMs were later designed to avoid the vanishing gradient that occurs with long sequences. A simplified version of the LSTM, the GRU was applied to resolve simple problems using shorter sequences. In 2016, [PERSON] et al. (2016) first proposed temporal convolutional networks (TCNs) for video-based action segmentation. In practice, TCNs have all the advantages of LSTMs as well as extended memory processing input based on dilated convolution architecture and residual connections, with higher classification accuracy than LSTMs.  
TCN architecture is based on dilated casual convolutions that enable an exponentially large receptive field. This is more suitable for sequence modeling based on sequence-to-sequence applications that require long effective memory, such as long- or medium-term wind power forecasting ([PERSON] et al., 2016). Dilated convolution is a means of increasing the receptive view of the network exponentially, as well as linear parameter accretion (Kal  
TCNs exhibit outstanding behavior with sequences of undetermined length, and the TCN architecture can assist engineers in managing information flows in incredibly long sequences. Consequently, TCNs have stronger learning capabilities and exhibit equal or better results compared to those of the RNN, LSTM, and GRU.  
To meet the large memory requirements for DLNs, TCNs use one-dimensional (1D) separable convolutions to factorize a standard convolution into a depth-wise and pointwise convolution. Typically, the TCN consists of three parts: dilated causal convolutions, nonlinear activation, and residual connections. A causal convolutional network is used with 1-dimensional fully convolutional network architecture. A key characteristic is that the output at time \(t\) is only convolved with the elements that occurred before \(t\). In 2020, [PERSON] et al. [23] used a TCN for weather state predictions in a comparative experiment conducted with an LSTM. Notably, the results demonstrated that the TCN outperformed other models, including the RNN, LSTN, and GRU, in prediction tasks with time-series data. As shown in Figure 1, the TCN used the 1-dimensional convolutional neural network for short-term wind power prediction, showing that it not only retained the powerful ability of feature learning from both the weather data and electric power output, but was also suitable for processing large volumes of time series data.