# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 1 Introduction  
With the large consumption of fossil energy and the impact of global climate change, increasing attention has turned to renewable energy. As a kind of clean and renewable energy, wind power has been widely valued and promoted in the world. According to the World Wind Energy Association statistics, the total cumulative installed capacity from wind power around the world was approximately 539 GW by the end of 2017. However, with the expansion of wind power generation, its problems have gradually become more prominent. Due to the randomness, fluctuation and intermittence nature of wind speed, the wind power output has great uncertainty [1; 2]. Therefore, large-scale wind power integration will bring great hidden dangers to the safe and stable operation of the power system. In order to solve the above problems, it is necessary to effectively improve the controllability and predictability of wind power. Accurate wind power forecasting can provide an important basis for power dispatching and improve the utilization efficiency of wind energy resources. Since wind power has a direct relationship with wind speed, wind power forecasting can be achieved based on wind speed forecasting. The accuracy of wind speed forecasting is not only dependent on the forecasting method but also on the forecasting time horizon. In general, there are two main categories for wind speed forecasting based on the forecasting time horizon, namely, short-term forecast (time horizon of minutes, hours, and days) and long-term forecast (time horizon of days, weeks and months) [3]. The short-term forecast is important for the operation of wind turbines so that dynamic controls can be accomplished to increase the energy conversion efficiency and reduce the risk of overloading. The long-term forecast can provide important references for site location and planning of wind farms. In recent years, researchers have done many studies on the theory and methods of wind speed forecasting. According to the type of input variables of the forecasting model, the wind speed forecasting methods can be divided into physical forecasting methods, statistical forecasting methods and hybrid forecasting methods [4]. The input variables of the physical models are meteorological data from numerical weather prediction (NWP) and terrain feature data. Many physical models have been introduced, and the most popular three are Prediktor [5], LocalPred [6] and Previento [7]. Generally, physical methods have advantages in long-term forecast, while statistical methods have better performance in short-term forecast [1]. The input variables of the statistical models are long-term historical wind speed data. The statistical models achieve the forecasting of wind speed in the future by excavating the hidden change rules in historical wind speed data. Early statistical models include the autoregressive model (AR) [8], the autoregressive moving average model (ARMA) [9], the grey prediction model (GM) [10], etc. With the development of artificial intelligence techniques, support vector machines (SVM) and a large number of artificial neural network (ANN) algorithms have been introduced into wind speed forecasting. For example, [PERSON] et al. [11] introduced SVM to wind speed prediction and compared its performance with the multilayer perceptron (MLP) neural networks. [PERSON] et al. [12] used the ANN to forecast the wind speed in the region of La Venta, Oaxaca, Mexico, and the results showed very good accuracy for the short-term wind speed forecasting. [PERSON] et al. [13] used back propagation neural network (BPNN) to forecast the wind speed. [PERSON] et al. [14] compared three different ANNs (adaptive linear element NN, BPNN, and radial basis function NN) in one-hour ahead wind speed forecasting, and the results showed that even for the same wind dataset, the choice of best-performing model might not be the same with different evaluation metrics. Hybrid forecasting methods are formed by hybridizing physical methods and statistical methods together.