CAJ | 학술논문

本文利用人工神经网络对地埋管换热器出口水温的预测,出口水温对热泵的有效操作以及能源节约都至关重要。在系统模拟以及系统识别中,人工神经网络应用广泛。为了训练人工神经网络预测模型,采用有限的实验方法组为训练、测试数据。在此研究中,在输入层里,含有土壤导热系数、埋管间距、钻孔深度、管内水流量、地埋管换热器入口水温、输入热流量的大小及时间;地埋管换热器出口水温在输出层。网络中,反向传播学习三种算法分别为：Levenberg-Marquardt算法（LM）,比例共轭梯度算法（SCG）以及动量批梯度下降函数（GDM）,同时运用切线非传递函数,从而得出最佳方法。预测结果显示,最合适的演算法以及隐藏神经元的数量为LM-10。训练之后,均方根（RMS）为1%,方差值R2的绝对分数为99.9%,最大cov的变异系数比为25.7%。说明人工神经网络可以对地埋管换热器出口水温精确预测。
본문이용인공신경망락대지매관환열기출구수온적예측,출구수온대열빙적유효조작이급능원절약도지관중요。재계통모의이급계통식별중,인공신경망락응용엄범。위료훈련인공신경망락예측모형,채용유한적실험방법조위훈련、측시수거。재차연구중,재수입층리,함유토양도열계수、매관간거、찬공심도、관내수류량、지매관환열기입구수온、수입열류량적대소급시간;지매관환열기출구수온재수출층。망락중,반향전파학습삼충산법분별위：Levenberg-Marquardt산법（LM）,비례공액제도산법（SCG）이급동량비제도하강함수（GDM）,동시운용절선비전체함수,종이득출최가방법。예측결과현시,최합괄적연산법이급은장신경원적수량위LM-10。훈련지후,균방근（RMS）위1%,방차치R2적절대분수위99.9%,최대cov적변이계수비위25.7%。설명인공신경망락가이대지매관환열기출구수온정학예측。
This paper shows the applicability of Artificial Neural Networks (ANNs) to predict export water temperature of heat exchanger. Export water temperature of heat exchanger is important for the optimal control and energy saving operation of heat pump systems. ANNs have been used in varied applications and they are useful in system modelling and system identification. In order to train the ANNs, limited experimental measurements were used as training data and test data. In this study, in input layer, there are soil coefficient of thermal conductivity, buried tube spacing, drilling depth, water flow, buried tube heat exchanger entrance temperature and the size and the time input of heat flow; export water temperature is in output layer. The back propagation learning algorithm with three different variants, namely Levenberg-Marguardt(LM), Gradient Descent Method (GDM), and Scaled Conjugate Gradient(SCG).The best algorithm and neuron number in the hidden layer are found as LM with ten neurons. After the training, it is found that RMS is 1%, and R2 is 99.999% and coefficient of variation (coy) in percent value is 25.7%. So ANNs can be used for prediction of exoort water temoerature of heat exchanger as an accurate method.