# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 1 Introduction  
Under the pressure of environmental pollution and an energy crisis [1], the use of renewable energy sources, such as wind power, photovoltaic power, and biomass power, is rapidly increasing as alternatives to conventional sources [2; 3; 4; 5; 6; 7; 8]. Many countries have made several projections for large-scale integration of renewable energy sources into their grids [9; 10; 11]. Wind power generation is one of the fastest-increasing types of renewable energy generation [12; 13; 14; 15]. Due to the intermittent and variable nature of wind power, wind power prediction [16] is of great importance for the safety [17; 18], stability [19; 20], and economic efficiency [21; 22] of power grids. Wind power prediction (WPP) models can provide useful information about the upcoming wind power generation profile [12]. The Chinese government has issued relevant regulations requiring grid-connected wind farms to establish independent wind power prediction systems [23]. In order to guarantee the normal operation of the power grids and reduce additional maintenance costs, it is of great significance to study high-accuracy WPP methods, especially the short-term wind power prediction method [24]. The deterministic WPP techniques are mature and widely used, they can be divided into statistical and physical methods [12; 22; 25]. The traditional physical method is an analytical method, which achieves wind power prediction by a series of empirical formulas [24; 26]; it is simple and easy toimplement, but its accuracy is low. The statistical methods, on the other hand, predict the wind power based on the historical power generation data rather than the geographical parameters and the weather. In addition, its prediction model can be modified continuously according to the evaluation indexes. Hence, the prediction accuracy of these methods is typically higher than physical methods [27; 28]. The prediction models for such methods mainly include the moving average (MA) model, the autoregressive integrated moving average (ARIMA) model, the back-propagation (BP) neural network [27], the support vector machine (SVM) [29], and the recurrent neural network (RNN) [30]. In [27], a three-layer BP neural network prediction model was proposed for one-step prediction of wind speed of wind farms, and thus can provide reference for short-term wind power prediction. Aiming at solving the lag problem of wind power prediction. In [31], wavelet transform is adopted to decompose the input data into an approximate signal and some detail signals. After that, the BP neural network predicts future wind power data according to decomposed sub-signal. However, the wind power is a non-stationary time series [32], which means the previously observed values will affect future values. Additionally, the input data is time-ordered for the prediction of the time series. The input nodes of the BP neural network are independent, which means that the BP network cannot learn the temporal relationship contained in input historical wind power data. It is usually not suitable to deal with the learning problem for the time sequence. In [29], the support vector machine (SVM) was used to predict the wind power generation. The SVM method can theoretically find global optimal predictions. However, when there is a large amount of data, the computational cost of the SVM method would increase sharply. Therefore, it is typically for the prediction with small samples. In [33], a recurrent neural network (RNN) based method was proposed to predict wind power by learning the time relationship contained in the time series. Unlike feedforward neural networks, the RNN is a variant of artificial neural networks, and its unique internal state that carries memory of past values makes an RNN able to form a directed graph along a sequence, and learn the temporal dynamic behavior for a time series. However, the tanh activation function may lead to gradient disappearance and gradient explosion [34; 35], which will affect the prediction performance.