Chilled water plants represent a significant component of energy consumption in institutional buildings, where efficient operation is critical to reducing operational costs and environmental impact. This research investigates the potential energy savings and performance improvements achievable through the implementation of a predictive-adaptive chilled water flow control strategy in the chilled water system of the University of Nottingham Malaysia, with a detailed focus on Block B, a representative office building. An integrated detailed numerical model coupling building thermal dynamics with chilled water plant operation was developed and rigorously validated against experimental data collected from a scaled prototype Heating, Ventilation, and Air Conditioning facility. Two flow control strategies were analysed: the conventional constant flow control currently employed and the proposed predictive-adaptive control, which dynamically adjusts chilled water flow rates based on real-time thermal loads at individual zones. Results demonstrated that the predictive-adaptive strategy significantly outperformed the constant flow approach, achieving approximately 46% reduction in chilled water consumption and 54% reduction in total energy use. Thermal comfort compliance improved significantly, with indoor temperatures maintained within the acceptable comfort range for about 85% of operational hours under the adaptive control, compared to only 21% under constant flow. The adaptive system further enhanced chiller efficiency through better load matching and reduced pumping energy, contributing to substantial carbon emissions reduction. The findings highlight the critical role of intelligent flow control in optimizing chilled water plant performance, offering a viable pathway to enhance energy efficiency, occupant comfort, and environmental sustainability in large-scale building cooling systems. Limitations of the study include the focus on a single building in a tropical climate and assumptions inherent in the modelling approach. Future research is recommended to explore the applicability of predictive-adaptive flow control across diverse climates and building types, integrate advanced machine learning algorithms, and evaluate long-term field performance. This study provides valuable insights and practical recommendations for the design, control, and operation of chilled water systems, supporting institutional efforts to reduce energy consumption and greenhouse gas emissions in building operations.
Vevekananda a/l Pasupati (Sat,) studied this question.