DocumentCode :
2670652
Title :
Load forecast calibration method for large-scale electricity-dependent Corporation
Author :
Feng, Gao ; Chuan, Zhang ; Hui, Xu ; Dianmin, Zhou ; Qiaozhu, Zhai ; Xiaohong, Guan
Author_Institution :
Syst. Eng. Inst., Xi´´an Jiaotong Univ., Xi´´an
fYear :
2008
fDate :
16-18 July 2008
Firstpage :
398
Lastpage :
402
Abstract :
The load of large-scale electricity-dependent corporation consists of the power loads of each production units; it has quite different characteristics from the load pattern of a large region. When a major maintenance happens, the load is much lower than normal level, so the result of the basic load forecast model looks much higher, and need to be calibration. In this paper, a load forecast calibration algorithm based on maintenance schedule is proposed. The algorithm aims at improving the precision of load forecasting when production line maintenance happens. The relationship between the load abnormality and maintenance is obtained by the way of power line load analysis, and calibration coefficients of each maintenance items are fixed using constrained least squares algorithm. The proposed algorithm is tested on real data, and prospective results are obtained.
Keywords :
calibration; least squares approximations; load forecasting; maintenance engineering; calibration coefficients; large-scale electricity-dependent corporation; least squares algorithm; load abnormality; load forecast calibration method; power line load analysis; production line maintenance; Algorithm design and analysis; Calibration; Large-scale systems; Least squares methods; Load forecasting; Load modeling; Predictive models; Production; Scheduling algorithm; Testing; Constrained least square; Load abnormality inspection; Load forecast calibration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
Type :
conf
DOI :
10.1109/CHICC.2008.4605770
Filename :
4605770
Link To Document :
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