**5. Conclusions**

This paper presents a model for predicting energy consumption in swimming facilities. The energy prediction model aims to become a dynamic energy benchmark for fault detection in swimming facilities. The investigation has been carried out by using multiple linear regression analysis (MLR) for a specific swimming facility located in Norway. The MLR method has formerly been recognized in predicting energy use in buildings but has also been applied to determine the parameters of thermal equations for outdoor swimming pools. The main findings of this study are:


inappropriate operation of technical installations may also cause problems such as degradation of equipment and the occurrence of sick building syndrome.

• This study only investigated one specific facility and future work should address the robustness of the model and transferability to other swimming facilities.

This study illustrates the strength of multiple regression analysis when applied as a dynamic and continuous energy benchmark. By applying simple input variables, an estimate of the expected power consumption, within an acceptable error range, can be made that reveals potential operational disruptions. The energy prediction model is simple and can be easily implemented in the automation system of a building. The prediction model does not require an operator with an engineering background and may serve as first-line supervision for the use of a dynamic energy benchmark for a facility. By applying this method in existing swimming facilities, the overall energy use may be greatly reduced as it provides the building management with improved knowledge about the energy performance of the building.

**Author Contributions:** Conceptualization, O.Ø.S.; methodology, O.Ø.S. and T.J.; software, O.Ø.S.; validation, O.Ø.S.; formal analysis, O.Ø.S.; investigation, O.Ø.S.; data curation, O.Ø.S.; writing original draft preparation, O.Ø.S.; writing—review and editing, O.Ø.S., T.J., B.A., J.S., L.G. and S.C.; visualization, O.Ø.S.; supervision, T.J., B.A., L.G. and S.C.; project administration, O.Ø.S. and J.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** The project is funded by COWI AS, the Research Council of Norway and COWIFonden.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** This data collection campaign was performed within the framework of a PhD study by COWI AS and NTNU SIAT. All the data are privately stored and will not be disclosed until the end of the study.

**Acknowledgments:** This work is a part of a doctoral project entitled "Optimizing Energy and Climate Systems in Buildings with Swimming Facilities", which is carried out as a cooperation project between the Centre for Sport Facilities and Technology at the Norwegian University of Science and Technology in Trondheim, Norway, and the engineering company COWI AS.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Appendix A**

**Figure A1.** Visual validation of the prediction model from September 2018 to June 2019. The prediction model includes the prediction interval in gray, measured power consumption in black and periods associated with operational disruptions in red.
