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《Automation of Electric Power Systems》 2004-06
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MULTI-MODEL AUTOMATIC SIFTING METHODOLOGY IN LOAD FORECASTING

Gao Feng , Kang Chongqing , Xia Qing , Huang Yonghao , Shang Jincheng , Meng Yuanjing, He Nanqiang (Tsinghua University, Beijing 100084, China) (Henan Province Electric Power Company, Zhengzhou 450052, China)  
The diversity of models is an important issue in load forecasting. To improve the precision for forecasting, it is necessary to distinguish between better models and bad ones. But this task is very difficult. This paper proposes a novel multi-model automatic sifting methodology to solve this problem. The odds-matrix method of the new algorithm is used to calculate the weight of each model, which reflects the 'optimality' of an individual forecasting model. Thus, the efficiency of each model can be differentiated via evaluating probability distribution function of the weights. Numerical studies show that this method is satisfactory in improving forecasting precision.
【Fund】: 国家重点基础研究专项经费资助项目(G1998020311);; 清华大学基础研究基金资助项目(JC2002018)
【CateGory Index】: TM715
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