Advanced analysis of operating parameters utilizing big data to improve building cooling equipment energy efficiency standards

Abstract

The rapid increase in building energy consumption has highlighted the critical contribution of building cooling/heating equipment, including room air conditioners and variable refrigerant flow (VRF) systems. However, existing energy efficiency standards for such equipment often specify testing conditions that differ from real-world usage scenarios and are unsupported by in-depth investigations, resulting in evaluations of equipment performance that do not align with actual performance. Therefore, the increasing availability of large-scale real-time monitoring datasets was leveraged in this study to develop an innovative technical approach to derive a more accurate description of VRF operation-related parameters. This approach was applied to inform suggestions for the enhancement of energy efficiency standards for VRF devices by collecting and preprocessing data, extracting operating parameters, conducting data analysis and comparisons, and discussing potential applications. The results of this study indicate that the type of building significantly influences the actual performance of a VRF and imply that large-scale monitoring data can provide a foundation for the revision of building equipment energy efficiency standards as well as future investigations of building equipment energy policies.