# 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
Thus, some studies have used optimization methods [87; 88; 89; 90], such as grid search, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization, (PSO) to determine parameters of SVM models. For the hydropower prediction, a hybrid method of ANFIS and gray wolf optimization (GWO) was designed to forecast hydropower generation [91]. In renewable energy, biomass and hydrogen can increase global energy sustainability and reduce greenhouse gas emissions [92]. It was reported that machine-learning algorithms, linear regression (LR), K nearest neighbor regression (KNN), support-vector machine regression (SVMR), and decision-tree regression (DTR) can be used to model gasification products without further revisions [93]. The gradient-boosted regression trees (GBRT) were employed to forecast the high heating value (HHV) of biomass with a dataset including 511 biomass samples [94]. Four models, namely polynomial regression, support-vector regression, decision-tree regression, and multilayer perceptron were used to predict CO, CO\({}_{2}\), CH\({}_{4}\), H\({}_{2}\) during biomass gasification and HHV outputs [95]. The numerical results illustrated that multilayer perceptron and decision-tree regression can provide better forecasting results than the other models. A new hybrid algorithm based on the combination of SVM and simulated-annealing (SA) optimization technology was designed to study the baking process in order to obtain a prediction model of the high heating value (HHV) in biomass [96]. The results of this study indicated that the SVM-SA hybrid model can improve the prediction performance. In wave-power-generation predictions, the prediction algorithms were used to predict the height of coastal waves in a relatively short period of time. A hybrid Improved complete ensemble EMD-ELM model was proposed to predict the wave heights [97]. The proposed model was an effective tool in forecasting wave heights. A Bayesian optimization with hybrid grouping genetic algorithms and an extreme-learning machine (BO-GGA-ELM) model was proposed to forecast the wave height and the wave energy flux at the target ocean [98]. Bayesian optimization was used to provide wave characteristics of the problem. In predictions of tidal-power generation, a univariate prediction method based on wavelet transform and support-vector regression (SVR) was designed to forecast the tide velocity and directions with high accuracy [99]. A power flow prediction method based on clustering technology was presented to obtain harmonic power flows [100]. These hybrid models involved wavelet and artificial neural networks (WNN and ANN) and the Fourier series combination based on Fourier series based on least square method (FSLSM) technology. The numerical results indicated that the model can obtain high forecasting accuracy. A method using probabilistic machine-learning techniques in the Bayesian framework was proposed to predict power flow [101]. The Gaussian process was used in this study to model tidal data. Based on ensemble empirical mode decomposition (EEMD) and least-squares support-vector machines (LSSVM), a forecasting models was developed to improve the accuracy of predicting tidal current speed (TCS) and tidal current direction (TCD) [102]. A two-stage method for modeling and prediction of tidal was presented [103]. This method used a fuzzy feature selection technique to extract the dataset from the tide velocity and directions. Then, support-vector regression was employed to make a forecast of tidal. For methods of geothermal energy predictions, [104] employed LSTM encoder-decoder model to forecast the geothermal energy. The LSTM encoder and the LSTM decoder were applied to deal with the learning of the past geothermal energy production and predictions of the future geothermal-energy generations, respectively. The predictions of geothermal-energy generations were conducted according to the output of the encoder and the batch size of LSTM with optimized periods and sequence lengths.