Neural Network Prediction Methods of Power Consumption for GSHP System with Bilateral Variable Flow

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• 英文论文题名：Neural Network Prediction Methods of Power Consumption for GSHP System with Bilateral Variable Flow
• 论文作者：LI Hui ; Duan Peiyong ; Wang Lin ; Yu Yu
• 英文论文作者：LI Hui ; Duan Peiyong ; Wang Lin ; Yu Yu ; Key Laboratory of Renewable Energy Utilization Technologies in Buildings ; Ministry of Education ; Shandong Normal University
• 年：2017
• 作者机构：Key Laboratory of Renewable Energy Utilization Technologies in Buildings,Ministry of Education;Shandong Normal University;
• 英文论文关键词：GSHP ; variable frequency ; HCMAC neural network ; prediction model ; power consumption
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（B）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
• 语种：英文
• 分类号：TP183;TU83
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：4
• 文件大小：185k
• 原文格式：D
• 会议级别：全国

摘要

In order to realize the optimal control of variable frequency circulating pumps for ground source heat pump(GSHP) system, it is necessary to build the prediction model of the total power consumption of GSHP system based on running data. Firstly, the power consumption analysis of GSHP system with bilateral variable flow is presented. Then a Hyberball Cerebellar Model Articulation Controller(HCMAC) prediction model of power consumption for GSHP system is established. The inputs of model include circulating pump frequency at user-side, circulating pump frequency at ground-source side, and current air conditioning load. Finally, according to the characteristics of the learning data of the GSHP system a differential parallel HCMAC learning method is proposed to improve the learning accuracy. The simulation experiments are performed according to the learning data provided by TRNSYS simulation platform. The experimental results show that the accuracy of differential parallel CMAC prediction model is better than that of the general HCMAC prediction model.
In order to realize the optimal control of variable frequency circulating pumps for ground source heat pump(GSHP) system, it is necessary to build the prediction model of the total power consumption of GSHP system based on running data. Firstly, the power consumption analysis of GSHP system with bilateral variable flow is presented. Then a Hyberball Cerebellar Model Articulation Controller(HCMAC) prediction model of power consumption for GSHP system is established. The inputs of model include circulating pump frequency at user-side, circulating pump frequency at ground-source side, and current air conditioning load. Finally, according to the characteristics of the learning data of the GSHP system a differential parallel HCMAC learning method is proposed to improve the learning accuracy. The simulation experiments are performed according to the learning data provided by TRNSYS simulation platform. The experimental results show that the accuracy of differential parallel CMAC prediction model is better than that of the general HCMAC prediction model.

引文

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