# Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine
## 5 Discussion  
### Findings  
As observed in the correlation analysis in Figure 2, the variables \"Steam Flow Rate at the collector output\" and \"Steam Flow Rate at the Boiler output\" had the strongest dependencies on the target variable studied with correlations of 99% and 97%, respectively. They were followed by the parameters \"Steam Pressure\" and \"Pressure Losses\", with respective dependencies of 87% and 84%. Finally, the variable \"Steam Temperature\" correlated with electrical power with a percentage of 73%.  
We then deduced that all predefined parameters had a significant correlation with the target variable. It is therefore appropriate to retain all of them to train and test the studied models.  
On the other hand, and in light of the performance results obtained in (Table 3), the model based on the long short-term memory (LSTM) algorithm offered a better quality of prediction of the electrical power parameter. This performance was seen in the R-squared metric score, which was the highest (\(\approx\)98.39%). However, the scores of the two metrics RMSE and MSE, which interpreted the errors, were, respectively, 0.241 MW and 0.058 MW.  
Similarly, and by training the model based on the CNN-LSTM algorithm, we were able to maintain a high R-squared score (\(\approx\)98.29%) and also minimize the error margins generated by LSTM to achieve an RMSE of 0.1199 MW and MSE equal to 0.0143 MW. This improvement confirmed that the CNN-LSTM hybrid mode is very suitable for power prediction.  
As for the convolutional neural network (CNN) algorithm, its performance was less acceptable given that its score was also high (R\({}^{2}\) = 94.66%), except that the margin of error was greater than that of the two previous models, with an RMSE of 0.4301 MW and an MSE of 0.1849 MW, which makes this model ranked third in our comparative study.  
To allow a more complete analysis of the models, the loss curves were drawn during the training phase and are presented in Figure 4 for the LSTM model, Figure 6 for the CNN model, and Figure 8 for the CNN-LSTM model. In this sense, the LSTM algorithm reached its maximum score (98%) after 100 epochs, the CNN-LSTM model reached a score of 98% after only 50 epochs, and finally, we recorded a score of 94% for the CNN model after 200 epochs, which also makes the calculation time of the CNN model much more important than the previous two models.  
According to the respective results obtained in Figures 5, 7 and 9, it is shown that the three chosen models offer good predictions of the target variable, with an advantageous prediction accuracy for the CNN-LSTM hybrid model compared to the others.  
It provides the possibility for the industrial unit to obtain electricity during periods when its thermal power plant does not cover local self-supply, i.e., when it is in deficit.  
For this type electrical energy deficit problem, there is a need to predict the power produced since in this case, the industrial unit will have to buy electricity, impacting its energy bill.  
In other words, the prediction of the target parameter \(\mathbf{P_{elec}}\) can allow industrial operators to act upstream at the right time on the processing parameters described in Table 2, primarily when the power produced is likely to decrease to under 35 MW.