# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 1 Introduction  
As one of the clean and renewable energy sources, wind power has developed rapidly all over the world during the last decade. In 2018, the global installed wind power capacity was 592 GW, which is expected to increase to 800 GW by the end of 2021 [1]. Wind speed is the most important factor affecting wind power generation [2]. Variability and uncertainty stemming from noncontrollable and nonadjustable wind speed bring tremendous difficulties to large-scale wind power integration and operation in power systems. A more accurate wind speed prediction can help reduce the negative impact of wind power integration and improve the efficacy and stability of power system operations [3; 4; 5]. To date, there have been three noteworthy approaches to wind speed prediction. The first approach is with respect to physical methods such as numerical weather prediction (NWP) models [6; 7], which primarily use mathematical models of the atmosphere and oceans to obtain the estimates of wind speed forecasts. NWP is typically a physical model with the advantages of high precision and strong basis. However, there exist a variety of challenges facing NWP, such as the difficulty of collecting meteorological data, the requirement of large-scale computing resources, and so on. The second approach is statistical methods, which make use of historical and measured data to establish input and output function models. These methods include the Kalman filter [8], autoregressiveintegrated moving average model (ARIMA), support vector regression (SVR), Gaussian regression, and grey prediction method [9; 10; 11; 12]. [PERSON] et al. [13] verified that the Kalman filter can significantly improve the output results of the model by adjusting the time step and prediction range of the filter, especially in a short-term prediction. [PERSON] et al. [14] proposed an ARIMA model based on repeated wavelet transform and compared them on different time scales to show its superiority. Finally, the third approach is to employ machine learning methods to establish a model between inputs and outputs, which is similar to the structure of synapse connection based on neural networks. Machine learning has been successfully applied to many domains such as cloud services [15; 16; 17], context classification [18], and wind power or wind speed forecasting [19; 20; 21; 22]. Different machine learning methods have an apparent influence on forecasting performance. The nonlinear can lead to poor forecasting results when an unsuitable machine learning method is utilized. The back-propagation (BP) neural network is a typical artificial neural network. [PERSON] et al. [23] proposed a wind speed prediction method based on Lorentz perturbation to optimize the weights and thresholds of the BP neural network by means of an improved particle swarm optimization (PSO) algorithm. [PERSON] et al. [24] proposed a wind speed prediction method based on singular spectrum analysis, convolutional neural network method, gated recurrent unit method, and support vector regression (SSA-CNNGRU-SVR). Deep learning has been widely applied to wind speed prediction recently. [PERSON] et al. used the negative correlation neural network model optimized by the PSO algorithm to predict wind speed [25]. [PERSON] et al. [26] adopted wavelet transform to process the original data of wind speed, and proposed a new cell update long short-term memory (LSTM) network to predict the future wind speed. [PERSON] et al. [27] presented an LSTM model with shared weights to forecast wind speed. [PERSON] et al. [28] used a method of combined multilayer feed-forward neural network (MLFFNN), fuzzy inference system (FIS), and SVR to predict wind speed. An approach of combined multilayer perceptron neural network and extreme learning machine was presented to predict short-term wind speed [29]. A combined deep learning neural network based on convolutional neural network (CNN) and radial basis neural network was presented to predict wind power generation for the next day [30].