Prediction of Variable-speed Compressor Power Based on Particle Swarm Optimization and Back Propagation Neural Network

Gong Qijian; Guo Yabin; Chen Huanxin; Cheng Yahao; Xu Shenming

doi:10.3969/j.issn.0253-4339.2020.01.089

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Prediction of Variable-speed Compressor Power Based on Particle Swarm Optimization and Back Propagation Neural Network

更新时间：2024-07-18

- Prediction of Variable-speed Compressor Power Based on Particle Swarm Optimization and Back Propagation Neural Network
- Journal of Refrigeration Vol. 41, Issue 1, (2020)
- 作者机构：
  
  华中科技大学能源与动力工程学院
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0253-4339.2020.01.089
  CLC：
- Published：2020
- 稿件说明：
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Gong Qijian, Guo Yabin, Chen Huanxin, et al. Prediction of Variable-speed Compressor Power Based on Particle Swarm Optimization and Back Propagation Neural Network[J]. Journal of refrigeration, 2020, 41(1).
DOI：

Gong Qijian, Guo Yabin, Chen Huanxin, et al. Prediction of Variable-speed Compressor Power Based on Particle Swarm Optimization and Back Propagation Neural Network[J]. Journal of refrigeration, 2020, 41(1). DOI： 10.3969/j.issn.0253-4339.2020.01.089.

摘要

本文针对变频压缩机的功率测量困难，测量误差大等问题，提出了一种仿真测量模型。利用粒子群算法寻找全局最优粒子，用它初始化BP神经网络的阈值和权值，测量变频压缩机的功率。本文共建立了3种仿真模进行对比，分别为BP神经网络模型、GA-BP神经网络模型和PSO-BP神经网络模型，然后分别通过3种模型的内插、蒸发温度外推和冷凝温度外推的测试方法对变频压缩机进行功率测量，对比分析其预测结果的平均相对误差和拟合程度。结果表明：基于粒子群算法优化的BP神经网络模型明显优于其他两个模型，特别是在冷凝温度外推测试中，较其他两个神经网络相对误差降低了1.11%、2.64%，3种测试方法下的平均相对误差均小于1%，拟合程度在0.9以上，表明基于粒子群算法优化的BP神经网络模型对变频压缩机功率有较好的测量能力，而且有较强的泛化能力。

Abstract

A prediction method based on simulation is proposed to reduce the difficulty and the large error in measuring the power of variable-speed compressor. The threshold and weight of a back propagation (BP) neural network were initialized by particle swarm optimization to measure the power of the variable-speed compressor. In this study

a total of three kinds of simulation models were established for comparison

i.e.

a BP neural network model

a genetic algorithm (GA)-BP neural network model

and a particle swarm optimization (PSO)-BP neural network model. Then

the power of variable-speed compressor was predicted through the interpolation of three models

as well as the extrapolation of evaporation temperature and condensation temperature. The predicted results and the average relative fitting degree error were compared and analyzed. The results showed that the BP neural network model based on the particle swarm algorithm optimization was superior to the other two models. For the extrapolation tests of condensation temperature

in particular

the relative error of BP neural network model was reduced by 1.11% and 2.64%

respectively

compared with the other two neural networks. For the three methods

the average relative error was within 1% and the fitting degree was above 0.9

indicating that the BP neural network model based on the particle swarm algorithm optimization can adequately obtain the power of variable-speed compressor and has a strong generalization ability.

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