# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 1 Introduction  
In the past few decades, the scale of wind power generation has rapidly increased. As reported by the Global Wind Energy Council (GWEC), the cumulative installed capacity of global wind energy reached 743 GW at the end of 2020 [1], an increase of 59% compared to 2019. Due to the random, nonlinear and nonstationary characteristics of wind energy, this rapid growth has led to a series of problems, such as the instability of power system balance and control [2]. More and more studies reveal that accurate short-term wind power prediction is helpful in optimizing scheduling and energy trading [3, 4]. The majority of scholars classify wind power forecasting methods into four categories: physical models, statistical models, machine-learning models and hybrid models [5, 6, 7]. Physical models are based on geographic environments and numerical weather forecasts (NWFs), such as wind speed, wind direction, temperature, humidity and pressure of 1-3 days, in advance. However, it requires rich expert experience and complex computation. It is mainly applicable to new power stations without historical data. The statistical model depends on the historical data observed by the power station to forecast future power generation, mainly using multiple regression methods, such as the Kalman filter [8], moving average (MA) methods, autoregressive (AR) or autoregressive moving average model (ARMA) [9, 10]. In recent years, machine-learning methods have been extensively studied and applied to short-term wind power forecasts, such as the support vector machine (SVM) [11, 12], random forest (RF) [13] and so on. [PERSON] et al. proposed a time series model based on a hybrid-kernel least-squares support vector machine [11], and [PERSON] et al. presented a model based on random forests for hour-ahead wind power forecast [13]. With the flourishing of artificial intelligence techniques, deep learning models with strong nonlinear complex mapping abilities are becoming more and more popular. Such as the deep belief network (DBN) [14; 15]. [PERSON] et al. proposed a bidirectional recurrent neural network (RNN)-based encoder-decoder scheme to learn efficient and robust embeddings for a high-dimensional multivariate time series [16]. [PERSON] et al. proposed a stacked recurrent neural network (SRNN) based on a parameter sine activation function (PSAF) for wind power prediction [17] using a convolutional neural network (CNN) [18] and long short-term memory (LSTM) [19]. [PERSON] et al. used a convolution neural network and long short-term memory for power prediction, and used a genetic algorithm to optimize the model [20]. [PERSON] et al. applied a quadratic mode decomposition and cascade deep learning to ultrashort-term wind power prediction [21]. [PERSON] et al. used a one-dimensional convolution neural network (CNN) and bidirectional short-term memory (BLSTM) networks to predict short-term wind speed at different heights [22]. [PERSON] et al. proposed a deep belief network (DBN) model for regression with an architecture of 144 input and 144 output nodes and was constructed using a restricted Boltzmann machine (RBM) [14]. [PERSON] et al. explored the feature extraction scheme based on three-dimensional convolutional neural networks (3D-CNNs) [18]. [PERSON] et al. showed a wind power prediction model based on the long short-term memory model [19]. In order to absorb the advantages of each model prediction method, some hybrid model methods are proposed. [PERSON] et al. [23] designed a new Laguerre neural network to build the hybrid forecasting model optimized by the opposition transition state transition algorithm. [PERSON] et al. [24] proposed a combined artificial neural network model with a particle swarm optimization algorithm and genetic algorithm. However, these statistical and machine learning methods are data-driven and limited by the data quality of the training set, so it is difficult to further improve the prediction accuracy. The data collected from the wind farm is large, but there may be redundancy among the data.