## 4 Discussion  
In the novel DRL algorithm, authors used stack cooling water temperature transmitter data [17], to initially predict itself using a modified neural network which we refer to as an initial LSTM. The neural network's weight was adjusted to set a reference RMSE\({}_{\gamma}\) for evaluation purposes, as shown in Figures 9 and 10. The obtained results demonstrated that the proposed DRL algorithm was able to select the best feature from the set of features obtained from the PEM electrolyser dataset. It is interesting to note that the algorithm used a small sample of data for the feature selection and incorporates a method of single (average) representation of features having multiple sensor data. Hence, it can be extended for cases with tens, hundreds or more feature subsets since it can reduce computation time.  
The selected feature is the cooling water temperature [17] and was used in the LSTM neural network designed to predict hydrogen temperature. The selection made by the DRL agent was validated with the aid of the Pearson coefficient [27] which confirmed that the cooling water temperature [17] indeed has the highest correlation coefficient (0.99) with the target sensor (hydrogen temperature transmitter [17]), Figure 18.  
The LSTM network was trained with optimised parameter set to achieve a training RMSE value of 0.09. The LSTM was then tested by predicting the hydrogen temperature at the outlet of the PEM electrolyser. The resulting prediction yielded a satisfactory accuracy with a testing (prediction) RMSE value of 0.1351 as shown in Table 4 and Figure 21.  
The designed LSTM when deployed to HMI or SCADA systems can perform intelligent (predictive) maintenance of the PEM electrolyser using the accurately predicted output (in this case, hydrogen temperature). This solves the problem with predictive maintenance mentioned earlier, where failure of sensor data causes inaccuracies in predictive maintenance models. The model can also be beneficial when deployed on operator visualisation systems to provide a warning alert when the sensor fails. According to the previous work [21], the critical temperature level for hydrogen in the electrolyser considered for the study is 72 \({}^{\circ}\)C. The intelligent maintenance model can activate the cooling water system when the hydrogen temperature approaches this high value using the predicted data. If the faulty sensor is not replaced within a time frame, the developed system can also be useful to shut down the electrolyser for maintenance personnel to replace the faulty sensor.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Study** & **Method Used** & **Testing RMSE** \\ \hline \multirow{2}{*}{Authors’ proposal} & Hybrid of deep reinforcement learning (DRL) and long short-term memory (LSTM) & 0.1351 \\ \hline \multirow{2}{*}{[PERSON] et al. [28]} & Auto-setup reinforcement learning algorithm & 0.2082 \\ \hline \multirow{2}{*}{[PERSON] et al. [29]} & Hybrid of DRL with extreme gradient boosting & 4.008 \\ \hline \multirow{2}{*}{[PERSON] et al. [30]} & Reinforcement learning based on double DQN & 0.3956 * \\ \hline \multirow{2}{*}{[PERSON] et al. [31]} & Hybrid ensemble DRL & 0.9327 \\ \hline \multirow{2}{*}{[PERSON] et al. [32]} & Dynamic ensemble model based on deep reinforcement learning & 1.7416 \\ \hline \multirow{2}{*}{[PERSON] et al. [33]} & Reinforcement learning & 0.5196 * \\  & hybridised with LSTM & 0.5196 * \\ \hline \hline \end{tabular}  
* RMSE is obtained from: \(\sqrt{mean\ squared\ error(MSE)}\).  
\end{table}
Table 5: Comparison of authors’ proposal with other methods found in the literature.  
Finally, Table 5 shows that the authors' proposal presents an excellent RMSE, fitting more accurately to the dataset than the other proposals found in the literature.