Optimal Chiller Loading Based on Collaborative Neurodynamic Optimization

Zhongying Chen, Jun Wang, Qing Long Han

Research output: Contribution to journalArticlepeer-review


Chillers are indispensable machines for heat removal and major sources of power consumption in heating, ventilation, and air conditioning systems. In this paper, a cardinalityconstrained global optimization problem is formulated to minimize power consumption for optimal chiller loading. The formulated problem is solved using a collaborative neurodynamic optimization method based on multiple neurodynamic models. Experimental results based on available actual chiller parameters are elaborated to demonstrate the superiority of the proposed approach to many baseline methods for optimal chiller loading.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Industrial Informatics
Publication statusAccepted/In press - 2022
Externally publishedYes


  • cardinality constraint
  • Collaboration
  • global optimization
  • HVAC
  • HVAC systems
  • Linear programming
  • Loading
  • neurodynamic optimization
  • Neurodynamics
  • Optimal chiller loading
  • Optimization
  • Power demand


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