Research on systematic analysis and optimization method for chillers based on model predictive control: A case study

Abstract

Chillers are key components of heating, ventilation, and air-conditioning (HVAC) systems and consume large amounts of energy in commercial buildings. This study conducted field tests on the operation performance of chillers in large-scale commercial buildings and analyzed the typical problems influencing their energy efficiency. Subsequently, based on the field test results, a systematic optimization method for chillers was proposed based on external factors, internal factors, and synergy control between them. For external factors, the cooling load prediction model comprising a linear model based on the time series was constructed using historical operation data; the proposed model could be applied to guide the control of chillers to match the building cooling demand accurately with high energy efficiency. For internal factors, the energy performance model of the chiller was constructed using historical operation data, and the operation characteristics of constant-speed and variable-speed centrifugal chillers were compared and summarized. Then, using the cooling load prediction model and energy performance model of the chillers, a new control strategy was proposed to improve the energy performance under different operating conditions throughout the year. Compared with previous operation modes in 2018 and 2019, the energy saving rates reached 19.21% and 5.22%, respectively, achieving considerable energy-saving and CO2 emission reduction effects.