# 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
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sources of** & **Models** & **Techniques** \\ \hline \multirow{9}{*}{Wind} & Artificial & \multirow{3}{*}{\begin{tabular}{c} Artificial \\ intelligence \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Gaussian process regression(GPR), Support vector regression(SVR), Artificial neural \\ networks(ANN) [16]; xGBoost regression, SVR, Random forest(RF) [18]; Least squares \\ support vector machine(SVM) [19]; SVR, ANN, Gradient boosting(GB), RF [20]; RF [21]; GB trees [22]; Multi-layer perceptron(MLP) [24]; Deep neural network(DNN)-principal component analysis [25]; Feedforward ANN [26]; Efficient deep convolution neural \\ network [27; 34]; Linear regression, neural networks, SVR [28]; Convolutional neural \\ networks(CNN) [29; 37]; DNN [30; 31]; Efficient deep CNN [32]; Stacked auto-encoders, back propagation [33]; Predictive deep CNN [35]; Improved radial basis function neural network-based model with an error feedback scheme [36]; AN and genetic \\ programming [38]; Long short-term memory(LSTM) [39; 42]; Improved LSTM-enhanced \\ forget-gate network [40]; LSTM-ANN [41]; Auto-LSTM [43]; Shared weight LSTM \\ network [44]; Ensem-LSTM [45]; Instance-based transfer-GB decision trees [114]; Extreme \\ learning machine(ELM) [115; 116]; Empirical mode decomposition(EMD)-stacked auto-encoders-ELM [117]; Pattern sequence-based forecasting [118] \\ \hline \multicolumn{2}{c}{Statistical} & \multicolumn{3}{c}{Physics-informed statistical [17]} \\ \hline \multirow{9}{*}{Solar} & Hybrid & Adaptive neuro-fuzzy inference system, Particle swarm optimization(PSO), Genetic \\  & algorithm [23]: Improved dragonfly algorithm-SVM [46]; Deep belief network with genetic \\  & algorithms [47]: Type-2 fuzzy neural network-PSO [48]; Multi-objective ant Lion \\  & algorithm-Least squares SVM [49]; Complete ensemble EMD-multi-Objective grey wolf \\  & optimization-ELM [50]; Variational Mode decomposition(VMD)-Backtracking \\  & Search-regularized ELM [51]; Coral reefs optimization algorithm with substrate layer, ELM [52]; Stacked extreme-learning machine [53]; VMD-singular spectrum analysis-LSTM-ELM [54]; Ensemble EMD-deep Boltzmann machine [55]; ELM-Improved complementary \\  & ensemble EMD with Adaptive noise-Autoregressive integrated moving average(ARIMA) [56]; Bayesian model averaging and Ensemble learning [57]; Sparse Bayesian-based robust \\  & functional regression [58]; Kernel principal component analysis-Core vector \\  & regression-Competition over resource [59]; Wavelet packet decomposition-LSTM [60]; \\  & Empirical wavelet transformation, Recurrent neural network(RNN) [61] \\ \hline \multirow{9}{*}{Solar} & Artificial & \multirow{3}{*}{\begin{tabular}{c} Gated recurrent units [64]; RF [65], SVR, RF [66]; RF [67]; RF, gradient boosted regression, \\ extreme GB [68]; Linear regression, decision trees, SVM, ANN [69]; ANN, SVM, GB, RF [70]; ANN [71; 72; 73; 74; 75; 76; 77; 78; 79; 110; 111; 12]; CNN [76]; DNN [77; 78; 79]; DNN, RNN, LSTM [80]; LSTM, \\  & auto-LSTM, gate recurrent unit(GRU), machine learning and statistical hybrid model [81]; LSTM [82; 84; 85]; LSTM, GRU [83]; Copula-base nonlinear quantile regression [88]; \\  & Multi-method [122]; Smart persistence [123]; \(K\)-nearest-neighbors, GB [124]; \\  & \(K\)-nearest-neighbors, SVM [125]; Angstrom-Prescott [126]; Multilayer feed-forward neural network [127]; Support vector classification [128]; GPR [129]; Regime-dependent ANN \\  & [130]; ELM [131]; Adaptive forward-backward greedy algorithm, leapForward, spikeslab, \\  & Cubist and bagEarthGCV [132]; Static and dynamic ensembles [133] \\ \hline \multirow{9}{*}{Hydropower} & Artificial & \multirow{3}{*}{\begin{tabular}{c} Bayesian linear regression [106] \\ \end{tabular} } \\  & intelligence & & \\ \cline{1-1}  & Hybrid & Grey wolf optimization-adaptive neuro-Fuzzy inference system [91]; Long-medium and \\  & & short-term, Bayesian stochastic dynamic programming [107] \\ \hline \multirow{3}{*}{Biomass} & Artificial & \multirow{3}{*}{\
\begin{tabular}{c} Linear regression, k-nearest neighbors’ regression, SVM, Decision tree regression [93]; \\ Gradient boosted regression trees [94]; Decision tree regression, MLP [95] \\ \end{tabular} } \\  & & Hybrid & SVM-Simulated annealing [96]; PSO-SVM [108] \\ \hline \multirow{3}{*}{Wave} & Artificial & \multirow{3}{*}{
\begin{tabular}{c} Fuzzy inference systems, ANN [109]; GPR [110]; Interval type-2 fuzzy inference system \\ [111] \\ \end{tabular} } \\  & & High-level & Improved complete ensemble EMD-ELM [97]; Bayesian optimization-grouping genetic \\ \cline{1-1}  & & algorithm-ELM [98] \\ \hline \hline \end{tabular}
\end{table}