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

被引：0
作者：
Li Hui [1 ]
Duan Peiyong [2 ]
Wang Lin [1 ]
Yu Yu [1 ]
机构：
[1] Minist Educ, Key Lab Renewable Energy Utilizat Technol Bldg, Jinan 250101, Shandong, Peoples R China
[2] Shandong Normal Univ, Jinan 250014, Shandong, Peoples R China
来源：
PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年
关键词：
GSHP; variable frequency; HCMAC neural network; prediction model; power consumption; SOURCE HEAT-PUMP;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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.
引用
收藏
页码：2023 / 2026
页数：4
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