4.7 Article

Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting

期刊

IEEE TRANSACTIONS ON POWER SYSTEMS
卷 26, 期 2, 页码 500-507

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2010.2052638

关键词

Adaptive method; deviation forecasting; secondary forecasting; short-term load forecasting; support vector machine (SVM)

资金

  1. National Natural Science Foundation of China [50777031, 50877041]
  2. Specialized Research Fund for the Doctoral Program of Higher Education [200800030039, TPWRS-00140-2009]

向作者/读者索取更多资源

Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China.

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