*1.4. Energy Prediction Methods*

The energy prediction methods include physical/engineering methods as well as statistical and artificial intelligence methods [24]. Lu et al. [25] addressed the design and analysis stage and proposed a physical model for a sports facility. Despite the challenge related to the required numbers of parameters, the model performed with a coefficient of correlation (*R*2) of 0.934. Westerlund et al. [26] showed that the engineering approach for estimating annual energy use gave satisfactory results in swimming facilities as well. The results from this study, with a prosaic and simple technical structure, illustrates the importance of heat recovery, where evaporation dominates the energy demand. The same observation was also revealed in the study by Lovell et al. [27] where an engineering model for the prediction of thermal performance for an outdoor Olympic swimming pool in Australia was developed. The model was based on the heat balance and performed with an accuracy of 67% of the predicted heating capacities. This was within a range of ±100 kW, which proved to be the most accurate model compared to other equivalent models. The study confirmed that evaporation dominated the energy demand of an outdoor swimming facility. The same physical and empirical equations are also applied in building performance simulation tools such as TRNSYS [28], ESP-R [29] and IDA ICE [30], among others. Manˇci´c et al. [31] determined the energy losses for a pool hall and pool, and later the optimal configuration of a polygeneration system [32], by modeling the system via physical and empirical equations in TRNSYS. Moreover, Duverge and Rajagopalan [33] investigated the energy and water performance of an aquatic center in Australia. They modeled the facility with the BPS tool EnergyPlus and recommended both solar heating and the use of vacuum filters in their study.

Yuce et al. [34] presented an artificial neural network approach for predicting the energy consumption and thermal comfort in an indoor swimming facility. The prediction was an application for an optimization-based control system for swimming facilities. Kampel et al. [35] proposed a statistical model for predicting the annual energy use of swimming facilities. It was developed through a multiple linear regression (MLR) analysis, and its purpose was to establish a tool for calculating energy performance indicators for the benchmarking of swimming facilities. In addition, the MLR method was also applied in the study by Duverge et al. [36]. One of the outcomes was that the usable floor area and the number of visitors were among the most influential variables for annual energy use.

While the simulation tools based on physical models and artificial neural networks, with different topologies and learning algorithms, can provide useful insights and efficiently predict target values, both frameworks are computationally costly and need case base adaptation. In the context of the practical use and implementation of energy prediction features among existing buildings, MLR has the potential to be in the middle ground with respect to computational cost and the opportunity to adapt it to the different target cases. MLR represents an easy-to-follow statistical method [37] which can explain a dependent variable, using multiple independent variables, but does not require in-depth knowledge of physical processes or training algorithms. It is easy to develop and implement [38] and is widely used in the prediction of energy use. For example, Safa et al. [39] presented a method to predict energy use in office buildings for the purpose of energy auditing. The study showed the capacity of simple models where the final regression model was based on outdoor temperature and occupancy with a monthly resolution. The model performed well with acceptable error, when assessing each of the four buildings in the study individually. Catalina et al. [40] developed a regression model for predicting the monthly space heating demand for residential buildings while another approach developed a generic equation of three variables for predicting the heating demand in apartments blocks [41]. The MLR method has also been applied with success in energy forecasting for swimming pool buildings [38,39].

The objective of this paper is to investigate and propose a method for energy prediction in swimming facilities, based on the MLR method. This approach has considerable potential for reducing the annual energy demand of both existing and new buildings by making the operating staff conscious of the performance of the building in relation to the design level. Buildings are only sustainable if they are operated and maintained properly [15].
