# A Survey of Machine Learning Models in Renewable Energy Predictions
## 4 Conclusions  
Due to concerns caused by climate change and global warming recently, the issue of renewable energy is booming. Thus, accurate prediction of renewable energy power becomes crucial, and plenty of related studies have been conducted. In addition, the complexity of various environmental conditions in renewable-energy-generation systems resulted in the inappropriateness of using closed \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measure-ments** & **Solar** & **Wind** & **Hydro-Power** & **Biomass** & **Geothermal** & **Wave** & **Tidal** & **Total** \\ \hline MAE & 17 & 26 & 1 & 3 & 1 & 2 & 0 & 50 \\ MAE & 12 & 23 & 0 & 1 & 0 & 0 & 3 & 39 \\ RMSE & 36 & 31 & 1 & 4 & 0 & 2 & 3 & 77 \\ R\({}^{2}\) & 9 & 8 & 1 & 3 & 1 & 1 & 0 & 23 \\ NRMSE & 9 & 4 & 0 & 0 & 0 & 0 & 0 & 13 \\ MSE & 2 & 8 & 0 & 1 & 1 & 0 & 0 & 12 \\ \hline \hline \end{tabular}  
NRMSE = Normalized root mean square error; MSE = mean square error.  
\end{table} 
Table 6: Measurements of forecasting accuracy used by more than 10 studies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sources of** & **R2/References** & **Average R2** \\
**Energy** & & \\ \hline
**Solar** & 0.705 [67]; 0.987 [80]; 0.9962 [87]; 0.9 [88]; 0.931 [89]; 0.99 [129]; \\  & 0.8506 [140]; 0.967 [131]; 0.99 [109] & 0.9240 \\ \hline
**Wind** & 0.99874 [16]; 0.995 [18]; 0.992 [44]; 0.878902 [45]; 0.9544 [46]; 0.9033 [52]; \\  & 0.99376 [107]; 0.9892 [108] & 0.96316 \\ \hline Hydropower & 0.79 [91] & 0.79 \\ \hline
**Biomass** & 0.999 [93]; 0.93 [94]; 0.9028 [96] & 0.9439 \\ \hline Geothermal & 0.86 [105] & & 0.86 \\ \hline Wave & 0.7823 [98] & & 0.7823 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: R\({}^{2}\) values in the energy prediction literature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sources of** & **R2/References** & **Average R2** \\
**Energy** & & \\ \hline
**Solar** & 0.705 [67]; 0.987 [80]; 0.9962 [87]; 0.9 [88]; 0.931 [89]; 0.99 [129]; \\  & 0.8506 [140]; 0.967 [131]; 0.99 [109] & 0.9240 \\ \hline
**Wind** & 0.99874 [16]; 0.995 [18]; 0.992 [44]; 0.878902 [45]; 0.9544 [46]; 0.9033 [52]; \\  & 0.99376 [107]; 0.9892 [108] & 0.96316 \\ \hline Hydropower & 0.79 [91] & 0.79 \\ \hline
**Biomass** & 0.999 [93]; 0.93 [94]; 0.9028 [96] & 0.9439 \\ \hline Geothermal & 0.86 [105] & & 0.86 \\ \hline Wave & 0.7823 [98] & & 0.7823 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Values of R\({}^{2}\) and MAPE. mathematical forms to describe renewable-energy-generation systems [136]. Therefore, applications of machine-learning models have been gradually popular in the renewable-energy predictions. This study reviewed machine-learning models in energy predictions in recent years and analyzed this topic from aspects of machine-learning models, renewable energy sources, data pre-processing techniques, parameter selection algorithms, and prediction performance measurements. Findings of this study can be summarized as follows. First, the applications machine-learning techniques to renewable energy have been increasing and the uses of artificial intelligence techniques and hybrid models in solar-energy and wind-energy predictions are the majority. Secondly, the decomposition method is more often employed than the other data pre-processing techniques for machine-learning models in renewable-energy predictions. Thirdly, extreme-learning machines and support-vector machines are two machine-learning models most frequently applying metaheuristics to parameter selection. Finally, machine-learning models with larger R\({}^{2}\) values lead to more accurate renewable-energy predictions in terms of MAPE. Some possible future research directions for machine-learning models in renewable-energy prediction are depicted as follows. First, it can be observed that most topics of machine-learning technology in renewable-energy predictions have been focused on solar or wind energy forecasting.