Enhancing Chiller Plant Modelling Performance Through NARX-Based Feature Optimization

• Published in: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023
• Main Author: Zabidi A.; Jaya M.I.; Hassasn H.A.; Yassin I.M.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175435859&doi=10.1109%2fICSECS58457.2023.10256365&partnerID=40&md5=d7b5381f6c0958a9ee8d0f4f6a54f228

The research focuses on the modelling chiller plants in air cooling systems of large buildings. The existing evaluation of prediction efficiency and identification of efficient components in chiller plants has been limited. The goal of this research is to develop a methodology for modeling chiller plants by utilizing key parameters from their components. The resulting model accurately simulates the actual chiller plant system and can be used by organizations to predict future events, aiding in preventative maintenance and reducing maintenance costs, especially in critical buildings like hospitals. The research process include compiling the chiller plant's history, simulating the machinery using a regression technique called NARX, selecting crucial parameters using an optimization technique (BPSO), and validating the model. This study enhances our understanding and management capabilities of these important cooling systems by addressing the challenges of efficient modeling and prediction accuracy in chiller plant systems. © 2023 IEEE.