• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (18): 158-166.doi: 10.3901/JME.2015.18.158

• 可再生能源与工程热物理 • 上一篇    下一篇

房间空调器长效运行性能预测及优化方案的研究

巫江虹, 刘超鹏, 梁志豪, 张才俊   

  1. 华南理工大学机械与汽车工程学院 广州 510640
  • 出版日期:2015-09-15 发布日期:2015-09-15
  • 基金资助:
    中国质量检测中心(2013IK133)和中国环境保护部环境保护对外合作中心课题(C/III/S/15/008)资助项目

Research on the Room Air Conditioner Long-term Performance Prediction and Optimization Strategy

WU Jianghong, LIU Chaopeng, LIANG Zhihao, ZHANG Caijun   

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640
  • Online:2015-09-15 Published:2015-09-15

摘要: 房间空调器实际运行过程中的能效是空调器持续节能的重要考核指标,为研究房间空调器长效运行性能特性,采用BP神经网络进行新机器的性能预测分析,获得在多因素影响下选择成本最优的空调长效性能的设计方法。BP网络的学习样本来自于旧机器实验室测试数据及房间空调器在真实运行工况下的在线监测动态衰减数据,通过对大量样本数据的学习,分析影响长效运行性能各因素的权重,确定长效运行性能的优化策略。对26台使用中的房间空调器进行性能进行测试,85%的样本作为数学模型的训练样本,15%的样本作为模型验证样本,结果表明,采用小样本训练的BP神经网络预测的长效综合评价值误差均在5%以内,预测结果收敛;经过对BP神经网络的权重分析,时间加权后的高温制冷性能、额定制冷性能、低温制热性能、额定制热性能归一化值所占决策权重分别为0.187、0.203、0.312、0.298。为验证BP网络的正确性,建立房间空调器在线性能监测系统软硬件及长效性能分析预测软件平台,通过大量和长期在用空调的实测数据,验证和优化BP网络。基于以上基础数据,进一步提出大数据关联规则挖掘模型应用于空调器长效分析的研究思路,应用于多因素影响下空调长效特性的优化设计。

关键词: BP神经网络, 长效节能, 大数据挖掘, 房间空调器

Abstract: Performance of occupied room air-conditioner(RAC) is an important evaluation index to estimate RAC continue energy saving efficiency. In order to investigate characteristic of RAC long-term performance(LTP) and acquire the cost optimation design methodology of high LTP in multi-factors impact condition, a BP neural network prediction method has been applied. The training sample of LTP prediction BP neural network acquired form experimental result of occupied RACs and data of RACs dynamic LTP on-line monitor system. By a large size of training sample, the decision weights of multi-impact factors and LTP optimation strategies can be obtained. The performances of 26 occupied RACs have also been tested. 85% of testing data ias served as training sample data and 15% of testing data ias served as validation data to LTP prediction BP neural network. The result indicated that the prediction is convergence and error is less than 5% during the BP neural network training by 22 samples. The decision weights of time weighted high temperature cooling, rated cooling, low temperature heating, rated heating normalized performance value are 0.187, 0.203, 0.312, 0.298, respectively. For further increasing the prediction precision, RAC performance online monitor system and LTP online data acquisition website has been established for data acquisition to validate LTP prediction BP neural network. Based on the acquisition database, a big data mining method has also been proposed in RAC LTP optimization design and investigation.

Key words: big data mining, BP neural network, long-term energy saving, room air conditioner

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