# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 1 Introduction
In the hybrid methods, NWP data is also used as the input data of the statistical models. [PERSON] et al. [15] used the output of the fifth generation mesoscale model (MM5 model) as the input variables of the ANN, and the experiment results showed that the hybrid MM5-neural network approach was able to obtain good short-term predictions of wind speed at specific points. [PERSON] et al. [16] then made improvements to the system proposed in Ref. [15], and used regression SVM instead of ANN to obtain the final prediction. [PERSON] et al. [17] trained the ANNs based on the measured wind speed series and on some NWP parameters, and an improvement of the performance had been reached, especially with the longer time horizons. However, the wind speed series has multiscale characteristics. The multiple frequency components existed in the wind speed series are always the challenging parts in forecasting. Currently, the concept of \"decomposition and ensemble\" (or \"divide and conquer\") [18; 19] has been used to solve this problem, and a number of various decomposition-ensemble based hybrid models have been developed and widely used. For instance, [PERSON] et al. [20] selected wavelet decomposition (WD) and wavelet packet decomposition (WPD) to decompose an original wind speed series respectively, and then used the ANN models to do the multistep forecasting in each subseries. [PERSON] et al. [21] combined empirical mode decomposition (EMD) and standard feed-forward neural network (FNN), and [PERSON] et al. [22] combined EMD and ANN. The new hybrid model all outperformed the conventional FNN or ANN model. But the WD is sensitive to the wavelet base function and decomposition level, and for the EMD, one major challenge is the frequent appearance of mode mixing [23]. Fortunately, there exists an improved method named ensemble empirical mode decomposition (EEMD), which makes up for the deficiency of EMD. The EEMD is an empirical, intuitive, direct and self-adaptive data processing method created especially for nonlinear and nonstationary signal sequences [3]. For example, [PERSON] et al. [24] proposed a wind speed forecasting method based on EEMD and optimized BPNN (GA-BPNN) for short-term wind speed forecasting, and computational results had shown the good performance of EEMD. In this study, we mainly focus on the wind speed statistical forecasting methods for short-term forecast. The first step is to determine the specific forecasting time horizon. When using statistical methods for direct multistep forecasting of wind speed series, in general, the longer the forecasting length, the lower the forecasting accuracy. Therefore, it is necessary to determine the predictable length of direct multistep forecasting. In this paper, the autocorrelation analysis method based on the maximal information coefficient (MIC) is introduced to measure the predictability of wind speed series. Then, the predictable time of statistical forecasting methods based on historical wind speed data is analyzed. The next step is to choose the wind speed statistical forecasting method. When the ordinary neural networks mentioned above process the wind speed series, the reading and processing of the input wind speed data are independent at each moment. These neural networks cannot fully consider the correlation of the wind speed series itself. When forecasting the wind speed at the next moment, they are unable to share the features learned from the previous input wind speed data. Therefore, the forecasting accuracy of these neural network models is limited. In order to make full use of the correlation between the data of wind speed series at each moment, this paper introduces a new long short-term memory neural network (LSTMNN) model for wind speed forecasting. The LSTMNN has a unique memory and forgetting mode. It can handle the long-term dependence of wind speed series very well, and effectively use the historical input information of the wind speed series.