# Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
## 1 Introduction  
Forecasting wind speed is a very difficult challenge compared to other variables of the atmosphere, and this is due to their chaotic and intermittent nature which causes difficulty in integrating wind power to the grid. As wind speed is one of the most developed and cheapest green energy sources, its accurate forecasting in the short term has become an important matter and has a decisive impact on the electricity grid. Both dynamic and statistical methods, and some hybrid methods that couple the two methods, were applied to forecast short term wind speed. Running high-resolution numerical weather prediction (NWP) models requires an understanding of many of the basic principles that support them, including data assimilation, knowledge on how to estimate the NWP model in space and time, and how to perform validation and verification of forecasts. This can be costly in terms of computational time. Reliable methods and techniques for forecasting wind speed are becoming increasingly important for characterizing and predicting wind resources [1]. The main purpose of any forecast is to build, identify, tune and validate time series models. Time series forecasting is one of the most important applied problems of machine learning and artificial intelligence in general, since the improvement of forecasting methods will make it possible to more accurately predict the behavior of various factors in different areas. Traditionally, such models are based on the methods of statistical analysis and mathematical modeling developed in the 1960s and 1970s [2]. The ARIMA model was used to forecast wind speed using common error ratio measures for model prediction accuracy [3]. Recently, deep learning in the machine learning community has gained significant popularity as it is considered a general framework that facilitates the training of deep neural networks with many hidden layers [4]. The availability of large datasets combined with the improvement in algorithms and the exponential growth in computing power led to an unparalleled surge of interest in the topic of machine learning. These methods use only historical data to learn the random dependencies between the past and the future. Among these methods, recurrent neural networks (RNNs) which are designed to learn a sequence of data by traversing a hidden state from one step of the sequence tothe next, combined with the input, and routing it back and forth between the inputs [5]. Long short-term memory based recurrent neural network (LSTM-RNN) has been used to forecasting 1 to 24 h ahead wind power [6]. A comparison was made between LSTM, extreme learning machine (ELM) and SVM. The results have shown that deep learning approaches are more effective than the traditional machine learning methods in improving prediction accuracy through the directional loop neural network structure and a special hidden unit [7]. Studies have suggested that coupling NWP models and artificial neural networks could be beneficial and provide better accuracy compared to conventional NWP model downscaling methods [8]. Numerical weather prediction method (NWP) is one of the most used methods, and it is suitable for long term rather than short term and medium term forecast due to the large amount of computation [9]. There has been analysis of the wind speed forecasting accuracy of the recurrent neural network models, and they have presented better results compared to the univariate and multivariate ARIMA models [10]. Linear and non-linear autoregressive models with and without external variables were developed to forecast short-term wind speed. Three performance metrics, MAE, RMSE and MAPE were used to measure the accuracy of the models [11]. LSTM method was used to forecast wind speed and the results were compared to traditional artificial neural network and autoregressive integrated moving average models, the proposed method proved better results [12].