## 5 Conclusions  
In this work, a novel DRL algorithm has been presented to apply artificial intelligence-based maintenance in electrolysers. The main contribution and novelty of the study lies in the DRL algorithm itself which is subsequently hybridised with a predictive LSTM model as follows:  
* Novel DRL algorithm: This selects the feature with the highest correlation with the hydrogen temperature transmitter (TT121), whose data is to be predicted in a PEM electrolyser. One of the novelties of the DRL algorithm lies in the method of evaluating each selected feature by the action of its internal agent, as discussed in Section 2.4 and shown in Figures 8-10. The evaluation method is based on the comparison of a reference RMSE with the one obtained from the selected features. The DRL algorithm is also unique such that when the agent needs to select a feature subset consisting of more than one sensor data, the average value which gives a single representation of the combination is used for evaluation in the algorithm. This saves computation time.
* LSTM predictive model: The feature selected by the DRL algorithm is used by the LSTM model to predict the hydrogen temperature for use in predictive (smart) maintenance of the electrolyser. Parameters of the main LSTM were also studied using illustrative plots, as shown in Figure 19, to obtain an optimised combination to reduce the prediction error.  
This study shows the benefits of reinforcement learning for the intelligent maintenance of renewable energy systems such as the electrolyser. This approach improves the durability of such equipment and ensures safety during the production of hydrogen, which is known to be highly inflammable.  
The limitation of this study is that the DRL-LSTM hybrid model will need to be retrained to adapt to a different hydrogen system, such as an alkaline electrolyser or fuel cell, whose physical phenomena are different from those for which it was trained in this study. Therefore, further work is needed to develop models that can be adapted to these different systems.  
In addition, it is planned to implement the developed DRL-LSTM model (via TCP/IP communication) on the on-board devices of the electrolysers, such as the programmable logic controllers (PLC) and the human-machine interface (HMI), for maintenance purposes in the future.  
Conceptualization, A.A. and F.S.M.; methodology, A.A.; software, A.A.; validation, A.A. and F.S.M.; formal analysis, A.A., F.S.M. and J.M.A.; investigation, A.A., F.S.M. and J.M.A.; resources, A.A. and F.S.M.; data curation, A.A. and F.S.M.; writing--original draft preparation, A.A. and F.S.M.; writing--review and editing, F.S.M. and J.M.A.; supervision, J.M.A.; project administration, F.S.M.; funding acquisition, F.S.M. and J.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Government, grant (1) Ref: PID2020-116616 RB-C31 and grant (2) Ref: RED2022-134588-T REDGENERA.  
The data presented in this study are available on request from the corresponding authors. The data are not publicly available due to internal agreements between project partnerships.  
The authors declare no conflict of interest.