# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Limitations**} \\ \hline Physical methods & \(\bullet\) & Physical methods for wind forecasting use numerical weather prediction (NWP) to predict weather, considering the effects of atmosphere, local terrain, and wind farm layout factors. & \(\bullet\) & Needs a lot of weather experts to handle numerical weather data prediction. In case that accuracy of NWP is poor, the wind power generation forecasting becomes inaccurate. \\ \hline Statistical methods [2,3] & \(\bullet\) & Applies statistical methods to find the relationships between weather features and the predicted power. & \(\bullet\) & A specific statistical method cannot handle complex weather conditions affected by atmosphere and environment factors. \\ \cline{3-4}  & \(\bullet\) & Statistical methods include Bayesian, regression, and auto regression integrated moving average (ARIMA) models. & \(\bullet\) & Thus, enhanced learning schemes such as random trees and GDBT are proposed in order to increase the accuracy for wind power prediction. \\ \hline Hybrid methods [4, 5, 6, 7, 8, 9] & \(\bullet\) & Aggregate different weights of models to improve model performance by preserving advantages of each approach, such as combination of fuzzy logic approach, artificial neural network (ANN) and support vector machine (SVM), where SVM and fuzzy logic approach can complement each other and ensure superior results. & \(\bullet\) & These hybrid models have problems with stable prediction as their complex learning architecture may cause low efficiency, long training times and even under-fitting. \\ \hline Deep learning methods [10,11] & \(\bullet\) & Use convolution operation to extract the features of time series data and predict the output using classification results. & \(\bullet\) & The performance of the DLN model is constrained by the quality of data input and neural architecture design. \\ \cline{3-4}  & \(\bullet\) & Multilayer neural networks for multiclass classification exhibited superior results in wind power forecasting applications. & \(\bullet\) & To avoid constraints from data inputs, researchers have begun to study and propose new (NWP + DLN) models recently. \\ \cline{3-4}  & \(\bullet\) & Comparison with the traditional ANN, deep learning neural networks do not need extra unsupervised networks or data preprocessing (e.g., decomposition). & \(\bullet\) & The performance of the DLN model is constrained by the quality of data input and neural architecture design. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Four Major Approaches for Wind Power Forecasting. forecasting problems, as these deep learning networks (DLNs) do not need extra data pre-processing, i.e., decomposition, in order to retrieve features from datasets. Typically, wind power forecasting is subject to a power level output classification problem related to different spatial temporal weather data. Four major types of DLNs for time series data have been applied to wind power forecasting from the time-series sequence data input, namely the recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN). RNNs were the first neural networks to assist in analyzing and learning sequences of data. However, some problems with RNNs were raised during model training, including slow computation times on account of their recurrent nature. Particularly when using Relu or Tanh as the activation function, long sequence inputs (i.e., gradient exploding and vanishing problems) become difficult to process [14]. LSTM was later proposed to solve the gradient exploding and vanishing problems. Typically, LSTM is capable of learning lengthy time dependencies by using the forget, input, and output gates in the module. Similarly, LSTMs have some weaknesses, for example difficulty in applying the dropout algorithm.