# A Survey of Machine Learning Models in Renewable Energy Predictions
## 2 Machine-Learning Models in Renewable-Energy Predictions
### The Status of Machine-Learning Technology Used in Renewable-Energy Forecasting  
Table 2 lists papers related topics of using machine-learning models in renewable-energy predictions since 2017. For wind-energy predictions, statistical methods were used in the early stage [17; 18]. Recent studies employed artificial intelligence and machine-learning techniques in wind-energy predictions, such as support-vector machines [16; 17; 18; 19], random forest classification algorithms [20; 21], gradient boosting decision trees [22], adaptive neuro-fuzzy inference system (ANFIS) [23], artificial neural network [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38], and long short-term memory networks [39; 40; 41; 42; 43; 44; 45]. The machine-learning techniques were able to capture data trends in forecasting wind energy. In addition, hybrid algorithms have been developed to improve forecasting models effectively and efficiently by integrating data processing approaches and optimization algorithms into machine-learning models [46; 47; 48; 49]. Extreme-learning machines (ELM) have been used in forecasting wind-power generation [50; 51; 52; 53; 54]. Wavelet decomposition (WD), wavelet packet decomposition (WPD), and ensemble empirical mode decomposition (EEMD) were employed to eliminate influences of noise from original data and can effectively improve the accuracy of wind-speed predictions [55; 56]. [57; 58] used the Bayesian model-based method to predict hybrid wind power. The numerical results indicated that the Bayesian model-based method can provide more accurate results than the other forecasting models in forecasting hybrid wind power. [59] developed a dynamic integration method to forecast wind speed. The phase space reconstruction (PSR) was used to dynamically select input vectors of prediction models. Besides, the kernel principal component analysis (KPCA) was used to extract the nonlinear features of the high-dimensional feature space reconstructed by PSR. Then, the core  
Figure 1: The pie chart in terms of seven renewable energy sources. vector regression (CVR) model with parameters determined by the competition over resource (COR) algorithm is established. The empirical results revealed that the proposed model significantly increased the prediction accuracy and statistically outperformed other forecasting approaches. A hybrid model including WPD, convolutional neural networks (CNN), and long short-term memory (LSTM) was designed to predict wind speed and can provide satisfying prediction results [60]. A hybrid model containing empirical wavelet transform, long short-term memory network, and Elman neural networks (EWT-LSTM-Elman) was proposed and outperformed the other forecasting models in wind-speed predictions [61]. In the solar energy prediction method, solar irradiance can be considered as a time series with different time scales. The most common time series forecasting method was autoregressive moving average (ARMA) [62; 63]. Machine-learning models and deep-learning techniques, including support-vector machines [64; 65; 66; 67; 68; 69; 70]] and artificial neural networks (ANN) [71; 72; 73; 74; 75] have been booming data-driven prediction models. Deep learning includes CNN [76], deep neural network (DNN) [77; 78; 79], long short-term memory [64; 80; 81; 82; 83; 84; 85], and the other hybrid models used in multistep predictions of solar energy. A method for predicting solar radiation sequences was introduced by using multiscale decomposition techniques, such as empirical mode decomposition (EMD), integrated empirical mode decomposition (EEMD), and wavelet decomposition, to investigate several clear sky index data [86], and based on linear, the method performs an autoregressive process (AR) and a nonlinear method. The results showed that the WD hybrid model (WD-NN) performed the best in predicting solar radiation. In terms of applications of SVM to solar-energy predictions, parameter selection is crucial to the forecasting accuracy of SVM.