Performance Prediction of a Water-Cooled Centrifugal Chiller in Standard Temperature Conditions Using In-Situ Measurement Data

In this study, a regression model was developed using the thermo-regulated residual refinement regression model (TRRM) analysis method based on three years and four months of in situ data collected from two water-cooled centrifugal chillers installed in A Tower, Seoul, South Korea. The primary objective of this study was to predict the coefficient of performance (COP) of water-cooled chillers under various operating conditions using only the chilled water outlet temperature ($T_2$) and the cooling water inlet temperature ($T_3$). The secondary objective was to estimate the COP under standard temperature conditions, which is essential for the absolute performance evaluation of chillers. The collected dataset was refined through thermodynamic preprocessing, including the removal of missing values and outliers, to ensure high data reliability. Based on this refined dataset, regression analyses were conducted separately for four cases: daytime (09:00–21:00) and nighttime (21:00–09:00) operations of chiller #1 and chiller #2, resulting in the derivation of four final regression equations. The reliability of the final dataset was further validated by applying other regression models, including simple linear (SL), bi-quadratic (BQ), and multivariate polynomial (MP) regression. The performance of each model was evaluated by calculating the coefficient of determination ($R^2$), coefficient of variation of root mean square error (CVRMSE), and the p-values of each coefficient. Additionally, the predicted COP values under the design and standard temperature conditions were compared with the measured COP values to assess the accuracy of the model. Error rates were also analyzed under scenarios where $T_2$ and $T_3$ were each varied by ±1 °C. To ensure robust validation, a final comparison was performed between the predicted and measured COP values. The results demonstrated that the TRRM exhibited high reliability and predictive accuracy, with most regression equations achieving $R^2$ values exceeding 90%, CVRMSE below 5%, and p-values below 0.05. Furthermore, the predicted COP values closely matched the actual measured COP values, further confirming the reliability of the regression model and equations. This study provides a practical method for estimating the COP of water-cooled chillers under standard temperature conditions or other operational conditions using only $T_2$ and $T_3$. This methodology can be utilized for objective performance assessments of chillers at various sites, supporting the development of effective maintenance strategies and performance optimization plans.