Abstract
This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility’s energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple linear regression analysis is used to analyze data collected in a swimming facility in Norway. The resolution of the original training dataset was in 1 min time steps and during the investigation was transposed both by time-averaging the data, and by treating part of the dataset exclusively. The statistically significant independent variables were found to be the outdoor dry-bulb temperature and the relative pool usage factor. The model accurately predicted the power consumption in the validation process, and also succeeded in disclosing all the critical operational disruptions in the validation dataset correctly. The model can therefore be applied as a dynamic energy benchmark for fault detection in swimming facilities. The final energy prediction model is relatively simple and can be deployed either in a spreadsheet or in the building automation reporting system, thus the method can contribute instantly to keep the operation of any swimming facility within the optimal individual energy performance range.
Highlights
The objective of this paper is to investigate and propose a method for energy prediction in swimming facilities, based on the multiple linear regression (MLR) method
This study investigates the impact of several independent variables on the energy use of a swimming facility
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 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]
Summary
The EU has defined a target for reducing GHG emissions by at least 40% by 2030 compared to 1990 levels [1]. Their long-term goal is defined as “no GHG emissions” by. Regarding the building stock of swimming facilities in Norway [6], the overall excessive energy use is estimated to be 28%. This provides a considerable incentive for improvement initiatives
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