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

Content Provider	IEEE Xplore Digital Library
Author	Yang Wang Qing Xia Chongqing Kang
Copyright Year	1969
Abstract	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.
Sponsorship	IEEE Power Engineering Society
Starting Page	500
Ending Page	507
Page Count	8
File Size	592675
File Format	PDF
ISSN	08858950
Volume Number	26
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Support vector machines Artificial neural networks Economic forecasting Power systems Weather forecasting Power system planning Time series analysis Performance analysis Algorithm design and analysis support vector machine (SVM) Adaptive method deviation forecasting secondary forecasting short-term load forecasting
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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