摘要
针对相变储能机组换热性能的非线性影响因素,分析了室内外不同环境因素下机组运行工况的性能系数(COP)及蓄/放能特征,并在焓差实验室进行机组性能测试。基于实验数据,建立了不同神经网络结构预测模型,预测机组COP及蓄/放能量;通过预测值与实验值结果对比,两者相关系数大于0.99,平均相对误差小于2%,平均均方差低于0.2%。研究结果表明,神经网络方法可以准确地预测相变机组储放能过程及对应的性能系数。
energy saving potential of latent heat storage unit was studied（LHSU） by using neural network. Experiments were conducted in an enthalpy difference laboratory to simulate the performance of LHSU. Different neural network structures were built to predict the influence of nonlinear factors including air flow rate, air temperature. Coefficient of performance （COP）, energy storage and discharge rates on the unit＇s operation performance. Results showed that the predictions agreed well with the experimental data with correlation coefficients in the range of 0.99-1.00, mean relative errors below 2% and very low root mean square errors.
出处
《土木建筑与环境工程》
CSCD
北大核心
2014年第5期66-70,共5页
Journal of Civil,Architectural & Environment Engineering
基金
国家高技术研究项目863计划(2012AA052503)
湖南省战略新兴产业科技攻关项目(2012GK4069)
湖南省科技厅科技计划重点项目(2013WK2011)
长沙科技计划项目(K1403142-11)
关键词
神经网络
相变储能
储能量
放能量
性能系数(COP)
neural network
phase change energy storage
energy storage
energy discharge
coefficient of performance.
