# A Survey of Machine Learning Models in Renewable Energy Predictions
## 2 Machine-Learning Models in Renewable-Energy Predictions
### Parameter Selection of Machine-Learning Models in Renewable-Energy Predictions  
Parameter selection influences performances of machine-learning models a lot. Most machine-learning models have more than two parameters. The trial-and-error method is not feasible. Thus, metaheuristics have been a popular way to seek proper parameters of machine-learning models. Basically, the forecasting error functions served as objective functions of metaheuristics for optimization. For each iteration, a new set of parameters are generated and used by a machine-learning model to make a forecast. Then a new foresting error is produced [47]. Sometimes a validation dataset is divided from the training dataset to prevent the overfitting of the training process [148]. [PERSON] et al. [91] used the gray wolf optimization (GWO) method to select conjunction parameters of the ANFIS model to predict hydropower generation. GWO can significantly improve the forecasting performance of ANFIS. [PERSON] et al. [50] employed extreme-learning machines with multi-objective grey wolf optimization (MOGWO) to achieve effective wind predictions. [PERSON] et al. [87] proposed an improved moth optimization algorithm (IMFO) to optimize the parameters of the SVM model for predicting photovoltaic-power generations. [PERSON] et al. [88] used firefly algorithm (FFA) to determine parameters of SVM models in forecasting solar radiation. Numerical results indicated that the proposed SVM-FFA model outperformed the other forecasting models in terms of forecasting accuracy. Particle swarm optimization (PSO) is a popular method used to determine parameters in wind and solar forecasting models [23; 48; 89; 142; 144]. [PERSON] et al. [89] used whale optimization, PSO, and Bat algorithms(BAT)for parameter determination of SVM models in solar-radiation predictions. The numerical results showed that support vector machines with bat algorithms (SVM-BAT) models performed the best in terms of the prediction accuracy and the rate of convergence. [PERSON] et al. [145] designed an empirical regression model with the artificial bee colony (ABC) algorithms to predict global solar radiation. The forecasting model depended on durations and angles of sunlight to make a forecast. The artificial bee colony algorithms were employed to selected parameters of empirical regression models. [PERSON] et al. [46] conducted short-term wind-power predictions by using an improved dragonfly algorithm to select parameters of the SVM model. [PERSON] et al. [96] presented a hybrid model of support-vector machine and simulated annealing (SA) to forecast the HHV of the biomass. The SA was used to decide SVM parameters, and the proposed hybrid model can provide promising forecasting results. [PERSON] et al. [47] presented a deep belief network with a genetic algorithms (DBNGA) model to predict wind speeds. The genetic algorithms was used for parameter selection of deep belief networks [PERSON] et al. [51] proposed a new a hybrid model, which contains variational mode decomposition (VMD), backtracking search algorithms (BSA) and regularized extreme-learning-machine (RELM) techniques in wind-speed predictions. The BSA technique was employed to select parameters of RELM models. [PERSON] and [PERSON] [49] used the multi-objective ant lion algorithms to determine parameters of ELM (Extreme-learning machines)models in the wind-speed predictions and can provide satisfying forecasting results. [PERSON] et al. [52] used coral-reef algorithms and ELM for wind-speed predictions. The coral-reef algorithms were employed to select parameters of ELM models. [PERSON] et al. [98] used Bayesian optimization to obtain ELM parameters in the ocean-wave prediction system. [PERSON] et al. [38] developed a short-term wind-energy prediction method by artificial neural networks with genetic programming for parameter selection. [PERSON] et al. [103] used modified harmony search (MHS) to determine parameters SVR in power-flow predictions.