Application of Hopfield Neural Network to the optimal chilled water supply temperature calculation of air-conditioning systems for saving energy

Abstract

The values of chilled water supply temperatures of chillers indicate their load distributions due to the chilled water return temperatures of all chillers being the same in a decoupled air-conditioning system. This study employs Hopfield Neural Network (HNN) to find out the chilled water supply temperatures of chillers for solving Optimal Chiller Loading (OCL) problem. HNN overcomes the flaw that Lagrangian method is not adaptable for solving OCL as the power consumption models include non-convex functions. The chilled water supply temperatures are used as the variables to be solved for the decoupled air-conditioning system and solves the problem by using HNN method to improve this defect. After analysis and comparison of the case study, it has been concluded that this method not only solves the problem of Lagrangian method, but also produces results with high accuracy. It can be perfectly applied to the operation of air-conditioning systems.