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Optimal Chiller Loading Based on Collaborative Neurodynamic Optimization
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AI
DOI：10.1109/TII.2022.3180080.png)
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Abstract
En 中文
Chillers are indispensable machines for heat removal and the primary 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.
Keywords:
Optimization
Neurodynamics
Linear programming
Power demand
Loading
HVAC
Collaboration
Cardinality constraint
global optimization
heating
ventilation
and air conditioning (HVAC) systems
neurodynamic optimization
optimal chiller loading (OCL)
Journal
IF:
9.9
Papers:
8.3K
Citations:
6.0W