# Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine
## 2 Related Works Linked to the Prediction of Electrical Power
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Application**} & \multicolumn{2}{c}{**Primary Energy**} & & \\  & & & **Side** & & **of the Case Study** & & & \\ \cline{3-8}
**Team** & **Year** & \multicolumn{2}{c}{**Prediction Technique**} & & & & & & \\ \cline{3-8}  & & & **Followed** & & & & & & \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2019} & **ARIMA** (_Auto-Regressive Integrated Moving Average_); & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [1] \\  & & & **SVM** (_Support Vector Machine_). & & & & & \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2019} & **SVR** (_Support Vector Regression_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [2] \\  & & & **ANN** (_Artificial Neural Network_). & & & & & \\ \hline \multirow{2}{*}{Sabri} & \multirow{2}{*}{2021} & **GRU** (_Gated Recurrent Units_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [3] \\  & & & **CNN** (_Convolutional Neural Network_) & & & & & \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2021} & **ANN** (_Artificial Neural Network_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} [4] \\  & & & **DNN** (_Deep Neural Network_). & & & & & \\ \hline \multirow{2}{*}{Kons-tantinou} & \multirow{2}{*}{2021} & **LSTM** (_Long Short-Term memory_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [5] \\ \cline{3-8} [PERSON] & \multirow{2}{*}{2021} & **Hybrid model LSTM-GR** (_Gaussian Process Regression_), & \multirow{2}{*}{**Bayesian Network.**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [6] \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2021} & **Fuzzy-GMDH** model optimized by [PERSON]. & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [7] \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2021} & **CNN** (_Convolutional Neural Network_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [8] \\ \cline{2-2} \cline{4-4} Al & & & _Decision Tree_ (**DT**), _Generalized Linear_ (**GL**), & & & & \\ \cline{3-8} Rayess & \multirow{2}{*}{2021} & _Gradient-Boosted Trees_ (**GBT**), and _Random Forest_ (**RF**). & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [9] \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2021} & _FBP_(_Facebook Prophet_). & \multirow{2}{*}{**SVM**(_Support Vector Machine_).} & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & [10] \\  & & & & & & \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2022} & _NOFGHW_ (_Novel Optimized Fractional Grey Holl–Winters_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [11] \\ \hline [PERSON] & \multirow{2}{*}{2022} & Hybrid model **LSTM-NP** (_Neural Prophet_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [12] \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2022} & **MLR** (_Multiple Linear Regression_). & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & \multirow{2}{*}{**X**} & [13] \\ \hline \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{2022} & **LSTM** improved by **EMD-PCA-RF**. & \multirow{2}{*}{**X**} & \multirow{2}{*}{**X**} & \multirow{2}{*}{} & [14] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of recent related works.