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《Mathematics in Practice and Theory》 2008-01
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Research and Application of Nonlinear Regression Correct Model in Sudden Change Load Forecasting

ZHAO Hai-qing(Department of Mathematics physics,North China Electric Power University,Baoding 071003,China)  
Nonlinear regression correct model prediction model is an effective forecast method of long-term load,it is a very good fit to the load of Conventional tendency,but for the load of sudden change or growth in the saturation stage,the error is larger. Based on the optimum and segmental of historical data,the nonlinear regression correct model is proposed,it is a good solution to this problem.Example shows that this model is applicable in the long-term load forecast,especially for sudden change load forecasting,it has a high forecast accuracy.
【CateGory Index】: O22
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【Co-citations】
Chinese Journal Full-text Database 9 Hits
1 ;Mid- and Long Term Load Forecasting Using Fourier Series Residual Correction[J];Anhui Electric Power;2010-02
2 HUANG Qing-she1,DENG Wen-bin1,ZHOU Bao-wen2,LI Qiong-qiong3,LI Jin-lu1(1.College of Electrical & Information Engineering,Changsha University of Science and Technology,Changsha 410076,China;2.Jinxian Power Supply Bureau,Jiangxi Electric Power Company,Jinxian 331700,Jiangxi,China;3.Jiangxi Branch,Guangzhou Shenyuan Electric Power Design & Survey Co.,Ltd.,Nanchang 330029,China);Grid Load Forecasting by Grey Model-based Equal Time Sequence[J];East China Electric Power;2010-08
3 YANG Fang(China Frist Tractor Group Co.,Ltd.,Luoyang 471003 China);Analysis and Calculating Method of Load Forecasting In Distribution Design[J];Coal Technology;2010-10
4 WANG Ke-liang1,2,YANG Li2,31.School of Management,Tianjin University,Tianjin 300072,China 2.School of Economics and Management,Anhui University of Science and Technology,Huainan,Anhui 232001,China 3.School of Management,University of Science and Technology of China,Hefei 230009,China;Study on power demand forecasting based on non-linear regression combined neural network[J];Computer Engineering and Applications;2010-28
5 BAO Guang-hui1,2,XIONG Yun-chao1,AI Qian1(1.Department of Electrical Engineering,Shanghai Jiaotong Univ.,Shanghai 200240,China;2.Shanghai Municipal Electric Power Company,Shanghai 200122,China);Approach for Forecasting Super-term Load Based on Immune Algorithm[J];East China Electric Power;2010-12
6 JIANG Tie-jun,ZHANG Huai-qiang(Department of Equipment Economy Management,Naval University of Engineering,Wuhan 430033,China);Research on the optimal combination forecasting of warship equipment construction cost based on information entropy[J];Ship Science and Technology;2011-01
7 XIAO Jun1,GENG Fang2,DU Bo-jun1,YU Bo1(1.Key Laboratory of Power System Simulation and Control of Ministry of Education,Tianjin University,Tianjin 300072,China;2.Chengxi Power Supply Company,Tianjin Electric Power Corporation,Tianjin 300072,China);Intelligent Recommendation of Urban Power Load Forecasting Models Based on Association Rules[J];Journal of Tianjin University;2010-12
8 HAN Hongwei1 LI Li2 1.Ningxia Electric Power Dispatch Centre,Yinchuanshi 750001;2.GD Shizuishan Power CO.Shizuishanshi 753202;The Gray Remaining Modified Model's Application to Mid-long Term Load Forecasting[J];Science & Technology Information;2010-07
9 ZHOU Quan1,REN Hai-jun1,LI Jian2,ZHANG Yun1,ZHOU Yong-yong1,SUN Cai-xin1,DENG Jing-yun1 (1.State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University),Shapingba District,Chongqing 400030,China;2.Sichuan Electric Power Company Meishan Company,Meishan 620010,Sichuan Province,China);Variable Weight Combination Method for Mid-long Term Power Load Forecasting Based on Hierarchical Structure[J];Proceedings of the CSEE;2010-16
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