Optimal Control Strategies for Multiple Chillers in a Building

Young-Sub Kim¹, Jin-Hong Kim¹, Hyeong-Gon Jo¹, CheolSoo Park¹, Eiji Urabe², Junghyon Mun², Yukung Shin², Yongsung Park²

1. Seoul National University
2. Samsung C&T

Published in Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024), edited by Ali GhaffarianHoseini, Amirhosein GhaffarianHoseini, Farzad Rahimian and Mahesh Babu Purushothaman. Springer Nature, Lecture Notes in Civil Engineering, volume 591, 2025, pages 1217 to 1224. DOI 10.1007/978-981-96-4051-5_116.

Abstract

This paper presents optimal control strategies of a cooling system for an existing building comprising several dozen chillers, pumps, and cooling towers. Particularly, such a large cooling system is designed to respond to highly time-varying cooling loads and guarantee a stable indoor environment. However, the existing control follows a predetermined rule-set for determining the number of operating devices which is presumably not the optimal operation for the facility. In this study, we introduced a physics-informed data-driven simulation model for the chillers that combines our domain knowledge of chiller dynamics with measured data. In other words, the model uses both operational datasets and the chiller’s specification information as a training dataset to address the chillers’ actual dynamics. The chiller model was developed using a Gaussian process emulator, one of the machine learning methods and can predict real-time time-varying coefficient of performance (COP). Subsequently, we applied Model Predictive Control (MPC) to the cooling system in the target building. MPC is designed to determine the optimal number of operating devices minimizing energy use by the system. The proposed control, MPC could save energy by 5–10% compared to the existing control, or rule-based control. An additional noteworthy benefit of the physics-informed data-driven model is that it can efficiently predict the chiller’s operating performance beyond the trained data because it contains physical knowledge. In other words, it exhibits better extrapolation behavior than a purely data driven model.

Keywords

Session

Presented in Track N, Saturday 9 November 2024, 16:15 to 18:15, room WA 224 B, Auckland University of Technology. Session chair Dr Aysu Kuru.

How to Cite

Kim, Y.-S., Kim, J.-H., Jo, H.-G., Park, C., Urabe, E., Mun, J., Shin, Y., & Park, Y. (2025). Optimal Control Strategies for Multiple Chillers in a Building. In A. GhaffarianHoseini, A. GhaffarianHoseini, F. Rahimian, & M. B. Purushothaman (Eds.), Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024) (Lecture Notes in Civil Engineering, Vol. 591, pp. 1217–1224). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-4051-5_116

About the Conference

Presented at SASBE 2024, the International Conference on Smart and Sustainable Built Environment, held in Auckland from 7 to 9 November 2024 and chaired by Professors Ali and Amirhosein GhaffarianHoseini, founders of GDI Academy. The version of record is published by Springer Nature; this page is the conference archive record kept by GDI Academy.