Optimal Chiller Loading Based on Collaborative Neurodynamic Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Journal / PublicationIEEE Transactions on Industrial Informatics
Publication statusOnline published - 3 Jun 2022

Abstract

Chillers are indispensable machines for heat removal and major sources of power consumption in heating, ventilation, and air conditioning systems. In this paper, a cardinality constrained 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.

Research Area(s)

  • Optimal chiller loading, neurodynamic optimization, global optimization, HVAC systems, cardinality constraint