# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 2 Related Work  
In this section, the literature on wind power forecasting [1; 9; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37], DSM [38; 39; 40] and electricity load and price forecasting [41; 42] is reviewed. The brief salient features of related literature are presented in Table 1.  
The wind power has a chaotic nature. Therefore, the incorporation of wind power in power supply systems is a risky task. To mitigate this risk, wind power forecasting is the most popular method. The wind power is forecasted using classical, statistical, data mining [9; 20; 21; 22; 23; 24; 25; 26; 27; 28] and artificial intelligence methods [1; 29; 30; 31; 32; 33; 34]. The accuracy of wind power forecasting is important to avoid demand-supply imbalance. Therefore, researchers are still competing to improve the wind power forecasting accuracy. In the literature, there are two types of wind power forecasting techniques:  
(1) _Time series (univariate)_: Past generation data are used to predict future generation [12; 24; 25; 36]. Univariate data are decomposed to make it multidimensional. Generally, data are decomposed by Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) or Wavelet Packet Transform (WPT). (2) _Multivariate_: Multiple exogenous inputs, such as Numerical Weather Predictions (NWP) (wind speed, wind direction, temperature, humidity, pressure, etc. ), hour, day of the year, etc. are used to predict wind power generation [1; 9; 26; 27; 28; 29; 30; 31; 32; 33; 34; 37]. The Artificial Neural Networks (ANNs) have widely used for modeling the highly fluctuating wind power data [1; 29; 30; 31; 32]. In [29], the authors forecasted wind power using ensemble ANN. The wind power time series is decomposed using DWT and related features are selected using conditional mutual information. Ensemble ANN is used for short-term wind power prediction. A Gaussian process based ensemble ANN is implemented in paper [30]. Five Gaussian processes and 52 sub-models of ANN are used to predict 48 h wind power. The authors of [1] proposed a bidirectional Extreme Learning Machine (ELM) for 6 h ahead WPF. Nelder-Mead simplex optimization algorithm is proposed for ELM's learning. ANNs combined with optimization techniques show a reasonable forecasting accuracy. However, the ANNs have a few limitations, such as over-training, sensitivity to initial set parameters and instability. The aforementioned methods are shallow learners, therefore, unable to learn the deep underlying structures hidden in the wind power data. To overcome the problem of shallow learning, the deep learning methods are introduced. Deep Neural Networks (DNNs) can model abstract features hidden in the data. The deep learning models have achieved better accuracy in WPF as compared to the ANN forecasting models [33; 34; 35; 36; 37]. The popular DNN methods used for WPF are Deep Belief Networks (DBNs) [33], Recurrent Neural Networks (RNNs) [34], Long Short-term Memory (LSTM) [35] and Convolution Neural Networks (CNNs) [36; 37]. In [33], ensemble DBNs are utilized as the wind power forecasting model. The wind power time series is decomposed by EMD and predicted by DBN. The building blocks of DBN are Restricted Boltzmann Machines (RBMs). Several RMBs are stacked together to construct a DBN. The DBN training process consists of two main steps: greedy layer-wise pre training and fine-tuning. By increasing the number of inputs, the DBN's computational complexity increase. The authors of [34] combined the RNN and infinite feature selection technique to address the WPF problem. RNN has recurrence operation and maintains data in the memory cells. CNN is superior to the DBN and RNN due to its less training time and efficient feature mining. CNN is a state-of-the-art deep learning method. It is the CNN's characteristic that it can extract the spatial features automatically. CNN is the most popular method for extracting features from the images and widely used in the field of computer vision.