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ABSTRACT
Recreational sports facilities are known to have a high level of energy consumption since the indoor-air conditions are crucial to athletes' performance. The thermal and moisture conditions are difficult to control without adequate models. A CFD model is used to simulate the thermal and moisture conditions of a gymnasium. This is coupled to a dynamic envelope model. Then, a reduced order model is developed to serve as the basis for an optimal energy control solution. Weather uncertainties are then evaluated within a dynamic control procedure that incorporates weather prediction errors at each time step.
INTRODUCTION
In 2017, about 39% of total U.S. energy consumption was due to residential and commercial buildings. The energy expenditure was approximately 38 quadrillion British thermal units (US Energy Information Administration). This fact motivates a number of serious efforts to optimize the energy use in these buildings as even a small reduction can lead to a significant impact. In this paper, we focus on sports facility buildings. Since significant sensible and latent heats are released through exercising activities and showers for those buidlings, high ventilation rates and cooling power are required to guarantee the indoor air quality falls within the comfort range for better performance of the athletes. Therefore, this class of buildings has a high level of energy consumption, and has a great potential for energy savings. Recently, recreational/sports centers have been studied for energy reduction purposes. Revel and Arnesano (Revel and Arnesano, 2014a, Revel and Arnesano, 2014b) related the energy use to the level of comfort perceived in two sports facilities: a gymnasium and a pool. They accounted for the activity levels and clothing and identified the uncertainty factors of the parameters that need to be examined, such as the air temperature, mean radiant temperature, air humidity, and air velocity, to accurately measure the predictive mean vote index, which is a quantification of human comfort level. Arnesano et al. (Arnesano et al. 2016) used the Sensor Optimization Unit (SOU) to optimize temperature sensor placement in large sport spaces. A lumped model that assumes a constant indoor temperature introduces significant errors at the sensing level. Considering the spatial temperature distribution via computational modeling or taking experimental measurements gave a better sensor positioning for these large spaces. Therefore, their...