## 5 Conclusions and Future Work  
The study presented in this paper considers a total of nine forecasting models, categorised into single and multistage ones. The models are trained twice, that is, with and without consideration of the external temperature as a feature. The results of the comparative study show that the multistage models generally perform better compared to the single models. However, the LSTM stands out and outperforms all other models, and the FBP-FBP model is the best performing multistage model. The results show the edge that recurring neural network models have over all other models, including hybrid models; however, further investigation and fine-tuning of the hybrid models can be done. As far as feature consideration is concerned, the temperature has little or no impact on the results, especially because the high-accuracy results were obtained from the LSTM without using the temperature as a feature, while for the FBP-FBP model, temperature had a minor effect on its accuracy.  
For future work, we suggest extending the analysis to a longer time horizon to capture long-term trends, comparing the residual forecasting method to other forecasting techniques, analyzing the impact of demand response strategies, assessing the effectiveness of energy efficiency measures, and exploring the generalisability of the results to other hospital settings and other societal sectors. By conducting these future studies, researchers can gain a more comprehensive understanding of energy demand patterns in hospitals, potential energy-saving strategies, and the applicability of the residual forecasting method in other settings.  
Figure 14: Histogram of residuals.  
Figure 13: Values of residuals.  
**Author Contributions:** Conceptualization, A.T., B.B., M.M.A.T., C.S.L., M.Z.A. and Q.H.A.; Data curation, A.T.; Formal analysis, A.T., B.B., M.M.A.T. and C.S.L.; Funding acquisition, A.T.; Investigation, A.T., Methodology, A.T., B.B., M.M.A.T. and C.S.L.; Software, A.T., B.B. and M.Z.A.; Visualization, A.T., M.A.S. and B.B.; Writing--original draft, A.T., B.B., M.M.A.T., M.A.S., C.S.L. and S.H.; Writing--review & editing, A.T., B.B., M.M.A.T., M.A.S., C.S.L., S.H. and Q.H.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by the Engineering and Physical Sciences Research Council (EPSRC) grants, EP/T517896/1.  
**Data Availability Statement:** Restrictions apply to the availability of the electricity consumption data. The data belong to Medway NHS Foundation Trust but were collected using systems provided by EnergyLogix. Data, however, can be made available with the approval of the corresponding author (A.T.), Medway NHS Foundation Trust, and EnergyLogix. As for the weather data, they were obtained from [24].  
**Acknowledgments:** The authors would like to acknowledge the support provided by EnergyLogix and Medway NHS Foundation Trust during the data collection phase. EnergyLogix is a Small to Medium Enterprise (SME) that provided the equipment used for collecting the electricity data for this study. This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) grants, EP/T517896/1.  
**Conflicts of Interest:** The authors declare no conflict of interest.