Developing Optimal Pre-Cooling Model Based on Statistical Analysis of BEMS Data in Air Handling Unit

Abstract Since the operating conditions of HVAC systems are different from those for which they are designed, on-going commissioning is required to optimize the energy consumed and the environment in the building. This study presents a methodology to analyze operational data and its applications. A predicted operation model is to be produced through a statistical data analysis using multiple regressions in SPSS. In this model, the dependent variable is the pre-cooling time, and the independent variables include the power output of the supply air inverter during pre-cooling, the supply air settemperature during pre-cooling, the indoor temperature-indoor set temperature just before pre-cooling, supply heat capacity,and the lowest outdoor air temperature during non-cooling/non-heating hours. The correlation coefficient R2 of the multipleregression model between the pre-cooling hour and the internal/external factors is of 0.612, and this could be used to provideinformation related to energy conservation and operating guidance.