NDLI: The Forecasting of Electrical Consumption Proportion of Different Industries in Substation Based on SCADA and the Daily Load Curve of Load Control System

Content Provider	IEEE Xplore Digital Library
Author	Dong Han Xinran Li
Copyright Year	2012
Abstract	In order to forecast the integrated load model of substation with a certain random time variation character and increase the accuracy of forecasting, this article put forward a forecasting method of electrical consumption proportion of different industries in substation based on the daily load curve. First of all, load sequence is decomposed into a number of different frequency stationary components by using EMD, according to the variation of the components, select the appropriate SVM parameter and support vector machine with different kernel function construction to forecast separately, and get the load curve forecasting value combined from each forecasted value by SVM. Then, classify the attributive for each industry and combine the industry equivalent daily load curve by using the fuzzy C means clustering principle. Finally, structure the related relation with the forecasted load curve, namely that obtain the final forecasted industry electrical consumption proportion in substation industry through the normalized projection of forecasted load curve calculated by industry typical feature vector. Refer to the simulation result, there are strong generalization ability and high precision for this method.
Starting Page	738
Ending Page	741
File Size	300223
Page Count	4
File Format	PDF
ISBN	9781467304580
DOI	10.1109/CDCIEM.2012.180
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industries Support vector machines Daily load curve Power system Substations Empirical mode decomposition Load forecasting Predictive models Support vector machine Load modeling
Content Type	Text
Resource Type	Article

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