**4. Discussion and Opportunities for Deployment of the Created Model**

Due to the importance of focusing on the operating phase when minimizing the environmental impact [10,54], and because operational irregularities are common in buildings [55], an implemented operational tool may have great potential for industry. For swimming facilities, this is especially important since inappropriate operation may also cause problems such as degradation of equipment and the occurrence of the sick building syndrome [56]. When applying the presented method to industry, the combination of a short-term training dataset and a few predictors makes this method especially useful. It means that a facility can develop a personalized model in short period of time with a minimum of sensors. In addition, the final energy prediction model is simple and can be deployed either in a spreadsheet or in the building automation reporting system. This method can therefore contribute instantly to keep the operation of a swimming facility within the optimal and expected individual energy performance range, which is fundamental for achieving the energy target for any building [57]. The MLR method, which is applied in this study, has formerly been recognized for predicting energy use in buildings [39] and has also been applied to determine the parameters of thermal equations for outdoor swimming pools [58]. With respect to the specific case of Jøa, the operational staff have to download the energy usage, the outdoor temperature and the pool usage. The deviation between the prediction and the measured energy use will give the operational staff an alarm if there is a potential flaw in the operation and enable them to detect the fault within a short period of time. However, the transferability with respect to the choice of independent variables must be further investigated in order to obtain a universal method for industry. Additionally, guidelines with respect to the implementation of the model should be provided.
