Improved energy management of chiller system with AI-based regression

This research aims to enhance energy management in commercial building air-conditioning systems, specifically focusing on chillers, which are significant energy consumers. This study evaluates various regularized regression models using comprehensive time series operating data from a system comprising five chillers of two distinct capacities. Compared with lasso and elastic net regression, ridge regression exhibits superior performance metrics when optimized with the appropriate hyperparameter. This makes it the most suitable method for modeling the system coefficient of performance (SCOP), thereby facilitating the development of an effective energy management plan. Key variables that strongly influence SCOP include the part load ratios of operating chillers, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. This study further identifies July as the month with the highest potential for performance improvement based on the predicted benchmark regions. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters. It highlights the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation, taking into account the distinct performance characteristics and time series features of individual system components. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.

Automated summary

This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.