Optimal Chiller Loading Based on Collaborative Neurodynamic Optimization
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Journal / Publication | IEEE Transactions on Industrial Informatics |
| Publication status | Online published - 3 Jun 2022 |
Link(s)
| DOI | DOI |
|---|---|
| Document Link | |
| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85131718807&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(9dac5236-5977-4403-b429-89710bfc303c).html |
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
Citation Format(s)
Optimal Chiller Loading Based on Collaborative Neurodynamic Optimization. / Chen, Zhongying; Wang, Jun; Han, Qing-Long.
In: IEEE Transactions on Industrial Informatics, 03.06.2022.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
