NDLI: The studying of combined power-load forecasting by error evaluation standard based on RBF network and SVM method

Content Provider	IEEE Xplore Digital Library
Author	Zhiping Fan Zhong Qin Tiansheng Hong Yufei Zhuang
Copyright Year	2009
Description	Author affiliation: College of Computer Science & Educational Software, Guangzhou University, Guangdong, 510006, China (Zhiping Fan; Yufei Zhuang) \|\| School of Civil Engineering, Guangzhou University, China, 510006 (Zhong Qin) \|\| School of Engineering, South China Agricultural University, Guangzhou, Guangdong, 510642, China (Tiansheng Hong)
Abstract	The load forecasting method usually starts from a single method, we usually improved prediction methods to get the better forecasting accuracy, but this often confined to the application of the method, combination forecasting method can achieve superiority of various methods, the forecast accuracy is higher than single forecasting method. In this paper, we used RBF neural network prediction method and support vector machine forecasting method.RBF neural network prediction method is the more popular method in recent years, it has the better generalization ability to the traditional neural network prediction method, It can effectively avoid local minima value and has a very good learning ability; SVM prediction method is transformed into one-dimensional nonlinear prediction of linear space, it has very precise calculation process and can meet the high forecast precision. Based on the combination of the two methods, not only from the Angle of artificial memory model prediction, and using the tight nonlinear model, ultimately meet the purpose of combined forecasting. The main innovation in this paper is that assess the result of every kind of prediction method by making the standards of error qualified, using the error rate to determine the weight of combination, finally, we can get the satisfactory results through an empirical analysis.
Starting Page	4016
Ending Page	4019
File Size	94809
Page Count	4
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5192606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	SVM method Prediction methods Artificial neural networks Predictive models RBF network Function approximation Support vector machines Load forecasting Neural networks Radial basis function networks Computer errors Mathematical model Power load Combined weight
Content Type	Text
Resource Type	Article

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