A Steady State ANN Model for the Operational Characteristics of a Variable Speed (VS) Direct Expansion (DX) Air Conditioning (A/C) System

Li Zhao; Deng Shiming

doi:10.3969/j.issn.0253-4339.2017.01.054

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A Steady State ANN Model for the Operational Characteristics of a Variable Speed (VS) Direct Expansion (DX) Air Conditioning (A/C) System

更新时间：2024-07-18

- A Steady State ANN Model for the Operational Characteristics of a Variable Speed (VS) Direct Expansion (DX) Air Conditioning (A/C) System
- Journal of Refrigeration Vol. 38, Issue 1, (2017)
- 作者机构：
  
  1. 上海理工大学环境与建筑学院
  2. 香港理工大学屋宇设备工程学系
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0253-4339.2017.01.054
  CLC：
- Published：2017
- 稿件说明：
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Li Zhao, Deng Shiming. A Steady State ANN Model for the Operational Characteristics of a Variable Speed (VS) Direct Expansion (DX) Air Conditioning (A/C) System[J]. Journal of refrigeration, 2017, 38(1).
DOI：

Li Zhao, Deng Shiming. A Steady State ANN Model for the Operational Characteristics of a Variable Speed (VS) Direct Expansion (DX) Air Conditioning (A/C) System[J]. Journal of refrigeration, 2017, 38(1). DOI： 10.3969/j.issn.0253-4339.2017.01.054.

摘要

在假设系统输出显、潜冷量的相对值在不同的蒸发器入口空气状态下不发生明显变化的前提下，本文针对实验用变速直膨式空调系统建立了稳态人工神经网络（ANN）模型，预测其在不同压缩机、风机转速组合下的系统输出，利用输出显、潜冷量的相对值可以消除室内空气状态对系统输出的影响。通过稳态实验获得数据训练、检测并验证ANN模型预测变速直膨式系统运行特性的准确性，并通过非训练状态点下的稳态实验验证所提出假设与ANN模型的适用性。ANN模型的训练、检测以及验证实验结果的最大误差均小于5%，平均误差均小于3%，表明该稳态ANN模型可以在训练状态点以及非训练状态点较为准确地预测变速直膨式系统的运行特性。

Abstract

In this study

an ANN model for an experimental VS DX A/C system was developed to predict its outputs with an assumption that the indoor air states will not influence the relative value of system outputs. Steady state experiments under a fixed indoor air state were carried out in this study to obtain the operational characteristics of the system for training and testing the ANN model and the ANN model developed was validated through steady state experiments under the non-training indoor air state. The experimental results showed that the max errors of prediction in training

testing and validating were below 5% and the average errors were below 3%

suggesting that the ANN model developed was capable of predicting the system outputs with satisfactory accuracy at both training and non-training indoor air states.

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Related Author

Yang Zhao

He Hongxia

Chen Yubo

Ge Yingying

Lin Wentao

Shi Jingfeng

Wang Yuanpeng

Liu Min

Related Institution

Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Tianjin University

R&D Centre, Qingdao Hisense Hitachi Air-conditioning Systems Co., Ltd.

School of Defense Engineering, Army Engineering University of PLA

School of Civil Engineering and Architecture, Zhejiang Sci-Tech University

Institute of Refrigeration and Cryogenic,Shanghai Jiao Tong University

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