Abstract
Accurate convective heat transfer coefficient (CHTC) is crucial to assess the night ventilation performance accurately. Previous experimental studies proposed CHTC correlations tailored for night cooling with diffuse ceiling ventilation (DCV) and mixing ventilation (MV). A holistic approach integrating the building energy simulation and optimization method was put forward to validate the experimentally proposed correlations, simulate and optimize the performance of night cooling with DCV and MV. The validation results demonstrated that the proposed correlations predicted the surface temperatures accurately with a maximum mean absolute error of 1.9 °C. For a typical air-conditioned office room, night cooling with DCV saved about 0.2 kWh/m2 total cooling energy per unit floor area (TCEC) more and provided a lower average predicted percentage of dissatisfied during the working hours (aPPD) by up to 3.1% than that with MV. Compared with the validated correlations, the empirical ceiling diffuser convection algorithm overestimated the night energy-saving potential by up to 0.5 kWh/m2 (13.8%) and yielded a maximum difference in the aPPD of 1.5%. Optimization significantly saved TCEC by 1.1 kWh/m2 (29.0%) for DCV and 0.9 kWh/m2 (25.7%) for MV while maintaining the aPPD within 10%, compared to respective base cases.
Highlights
Night ventilation (NV) performance is dependent on many factors [3,4], of which convective heat transfer coefficient (CHTC) and thermal mass level were two crucial parameters [5]
Regarding the current state-of-the-art, this study expanded the scope of previous work [32,33], where CHTC correlations for night cooling with diffuse ceiling ventilation (DCV) and mixing ventilation (MV) were proposed by experimental investigation
A holistic approach was put forward to validate the proposed CHTC correlations from previous experiments, simulate and optimize the performance of night cooling with DCV and MV in terms of energy and thermal comfort
Summary
Overheating is an ever-growing problem in buildings, especially for office buildings. Prior studies about air distribution principles mainly focused on ventilation effectiveness, indoor thermal comfort, and energy performance during the occupied hours [31]. Few previous studies investigated the performance of night cooling with different air distribution principles in BES tools. Guo et al leveraged the Non-dominated sorting genetic algorithm II [39] to optimize the design parameters related to the cool roof and night ventilation and the evolutionary optimization [40] algorithm to optimize the parameters associated with night mechanical ventilation for an air-conditioned room They did not consider the daytime AC temperature setpoint that determined the daytime cooling energy use, indoor thermal comfort, and the excess heat stored that NV can remove. The CHTC correlations in those studies were from the existing empirical correlations or not indicated, which limited the validity of their results
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