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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:ZHANG Yong-ming,YU De-liang,QI Wei-gui.Heating load interval forecasting approach based on support vector regression and error estimation[J].Journal of Harbin Institute Of Technology(New Series),2011,18(4):94-98.DOI:10.11916/j.issn.1005-9113.2011.04.019.
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Heating load interval forecasting approach based on support vector regression and error estimation
Author NameAffiliation
ZHANG Yong-ming Dept.of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
YU De-liang Dept.of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
QI Wei-gui Dept.of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
Abstract:
As the existing heating load forecasting methods are almostly point forecasting,an interval forecasting approach based on Support Vector Regression (SVR) and interval estimation of relative error is proposed in this paper.The forecasting output can be defined as energy saving control setting value of heating supply substation;meanwhile,it can also provide a practical basis for heating dispatching and peak load regulating operation.By means of the proposed approach,SVR model is used to point forecasting and the error interval can be gained by using nonparametric kernel estimation to the forecast error,which avoid the distributional assumptions.Combining the point forecasting results and error interval,the forecast confidence interval is obtained.Finally,the proposed model is performed through simulations by applying it to the data from a heating supply network in Harbin,and the results show that the method can meet the demands of energy saving control and heating dispatching.
Key words:  heating supply energy-saving  load forecasting  support vector regression  nonparametric kernel estimation  confidence interval
DOI：10.11916/j.issn.1005-9113.2011.04.019
Clc Number:TU831.2
Fund:

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