NDLI: Short-term power system load forecasting based on improved BP artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Zhang Xinbo Chen Jinsai
Copyright Year	2011
Description	Author affiliation: College of Information & Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China (Zhang Xinbo; Chen Jinsai)
Abstract	The accuracy of the forecast of power system loan, which is an important part of the forecast of short-term power system loan, will directly affect the economic of the power systems and its security and stability. The use of artificial neural network could get the similar feature like nonlinear system and use it on the short-term forecast. Researches about adding momentum into the improved BP network and combinating the same type of vague and mapping results when building input networks shows that it has better performance than standard BP algorithms. Meanwhile, after classification the input data categorize and dealing with the linear activate, putting these data to the corresponding sets, the result proved that its accuracy is higher than the standard of artificial neural network.
Starting Page	14
Ending Page	17
File Size	197080
Page Count	4
File Format	PDF
ISBN	9781424487271
e-ISBN	9781424487288
DOI	10.1109/CSAE.2011.5953161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	BP neural network Short-term power load forecasting Momentum The thought of similar day
Content Type	Text
Resource Type	Article

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