Optimal Chiller Loading Based on Collaborative Neurodynamic Optimization
Zhongying Chen, Jun Wang, Qing‐Long Han
Abstract
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.
Topics & Concepts
ChillerAir conditioningComputer scienceOptimization problemMathematical optimizationPower consumptionChiller boiler systemPower (physics)EngineeringWater chillerMathematicsMechanical engineeringThermodynamicsPhysicsGas compressorRefrigerantQuantum mechanicsBuilding Energy and Comfort OptimizationHeat Transfer and OptimizationAdvanced Multi-Objective Optimization Algorithms