Abstract
Active chilled beam (ACB) systems provide two highly-coupled cooling capacities due to its unique structure through combining primary air nozzle and cooling coil, whose energy consumption is difficult to be estimated and balanced against thermal comfort. This study considered the trade-off between energy consumption and thermal comfort as a multi-objective optimization problem and proposed a novel and practical solution by utilizing empirical energy models of the ACB system and an evolutional non-dominated sorting genetic algorithm II. The energy models are established for components of fans, pumps, and chillers based on fundamental equations which are validated by experimental data. The thermal comfort of each room is quantified by the predicted percentage dissatisfied (PPD) model. Chilled water flow rate, primary airflow rate, and room temperature in ACB systems are specifically chosen as control variables due to the control convenience. Besides, a parameterless selection strategy that considers both thermal comfort and energy consumption is proposed to select the most appropriate solution among Pareto optimal solutions. Three steady-state experiments with different heat load conditions are conducted. Compared to experienced operation, the proposed strategy demonstrates a maximum of 39.32% of energy saving and 12.21% of PPD reduction by increasing the water flow rate and room temperature, and reducing the primary airflow rate. Furthermore, this study investigates the distribution of capacities in ACB systems and suggests to assign more capacity to the cooling coil based on energy efficiency considerations. A good trade-off is achieved between energy consumption and thermal comfort through the proposed multi-objective optimization strategy.
Published Version
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