几种热力站二次供水温度预测模型的比较分析  

Comparison study of several prediction models of secondary supply water temperature in district heating thermal stations

作　　者：齐承英[1] 贾萌[1] 曹姗姗 孙春华[1] 夏国强[1] QI Chengying;JIA Meng;CAO Shanshan;SUN Chunhua;XIA Guoqiang(School of Energy and Environmental Engineering,Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学能源与环境工程学院,天津300401

出　　处：《河北工业大学学报》2023年第3期76-82,共7页Journal of Hebei University of Technology

摘　　要：供热系统通常通过调节热力站二次供水温度来满足热用户的需求。为了准确的获得二次供水温度的预测值,通过相关性分析和偏自相关分析确定预测模型的特征集;采用在线序列极限学习机(OSELM)、多元线性回归(MLR)、BP神经网络(BP)、支持向量回归(SVR)和极限学习机(ELM)模型进行短期二次供水温度的预测。对天津市某热力站的应用结果表明:预测特征集为室外温度和前28 h的历史二次供水温度数据;在训练样本容量较少和跨供暖季应用两种情况下,OS-ELM预测精度均最高,MAPE值分别为1.55%和0.47%。In order to meet heat users’varying demand,adjusting the secondary supply water temperature(SSWT)in thermal station is commonly used in district heating system.In order to obtain accurate prediction of SSWT,this study conducts correlation analysis and partial autocorrelation analysis to decide the feature set of prediction model.Online se-quential extreme learning machine(OS-ELM),multiple linear regression(MLR),BP neural network(BP),support vector regression(SVR)and extreme learning machine(ELM)are used to predict short-term SSWT.The proposed method is ap-plied in a thermal station in Tianjin.The results show that the feature set of SSWT prediction are outdoor temperature and historical SSWT in the previous 28 h.When the training sample size is small or cross heating seasons application,the OS-ELM model has the highest prediction accuracy,with MAPE values of 1.55%and 0.47%,respectively.

关 键 词：热力站 二次供水温度预测 OS-ELM 特征集 样本容量 

分 类 号：TU833[建筑科学—供热、供燃气、通风及空调工程]