# A Survey of Machine Learning Models in Renewable Energy Predictions
## 2 Machine-Learning Models in Renewable-Energy Predictions
### Data Preprocessing Techniques  
In energy forecasting, the modeling process could be divided into four stages, namely data preprocessing, determining proper hyper-parameters of models, training models, testing, and forecasting problems [49]. The data preprocessing includes removing data with missing values and data exceptions [57]. These data exceptions come from the lack of data caused by the abnormal collection mechanism of data collection. For example, the solar radiation data at night are meaningless to solar-power predictions [86]. Thus, data exceptions have to be filtered out. Table 3 lists data pre-processing techniques used by machine-learning models for renewable-energy predictions and illustrates that the decomposition method is more commonly used than the other data pre-processing techniques. For the data splitting, robust machine-learning models are generated by dividing original data into a training dataset, a validation dataset, and a testing dataset, or performing a cross-validation procedure during the modeling process [57; 95; 129]. The kernelized Mahalanobis distance (kWMD) method can effectively calculate the similarity of two unknown sample sets [71]. Data decomposition is used to preprocess the original signal and can increase the forecasting accuracy. Data decomposition decomposes a high-dimensional dataset into several low-dimensional sub-datasets and is often employed in signal processing problems [147]. Data discretization converts continuous data into discrete data and is especially useful in forecasting renewable energy by weather data [100; 130; 137; 138]. The encoding categorical features method removes impurities and redundancy in the original data and designs more efficient features to depict the relationship between problems and prediction models [67]. Feature selection is a technique to seek proper independent variables and remove the irrelevant or redundant attributes [43]. Data imputation of missing values is a process to replace the missing value with substituted values when null data happens in the modeling process [62; 139]. Data normalization refers to the adjustment of datasets expressed in different scales so that different datasets can be processed by machine-learning models. For example, weather attributes have to be normalized when conducting wind-speed predictions [16; 32; 38; 39; 46; 57; 66]. Data standardization is a process that converts data from different sizes to the same size, and Z-Score values are used to measure data scales [24]. Data transformation is a way to convert the state of the data. The Markov chains method is a typical data transformation approach [140]. Tables 4 and 5 show data pre-processing methods utilized by machine-learning models in predicting renewable energy in terms of types of renewable energy sources and variables, respectively. It can be observed that decomposition approaches for wind-energy predictions are the most often employed. The cause could be resulted from decomposing wind data with different frequencies and is able to improve forecasting performance of machine-learning models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sources of** & **Models** & **Techniques** \\ \hline Tidal & Artificial intelligence & Wavelet-SVR [99] \\ \cline{2-3}  & Hybrid & Wavelet and ANN, Fourier series based on least square method [100]; GPR-Bayesian [101]; Ensemble EMD-Least squares SVM [102]; Modified harmony search [103] \\ \hline Geothermal & Artificial intelligence & LSTM [104]; Multiple regression-ANN [112]; RF [113] \\ \cline{2-3}  & Statistical & Time dependent neural networks [105] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._