Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network
Joint Authors
Niu, Dongxiao
Lu, Yan
Xu, Xiaomin
Li, Bingjie
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition.
It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree.
Then this paper designed a forecasting model based on the chaos theory and RBF neural network.
It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point.
Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method.
Prediction results showed that most of the test sample load points could be accurately predicted.
American Psychological Association (APA)
Niu, Dongxiao& Lu, Yan& Xu, Xiaomin& Li, Bingjie. 2015. Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1073271
Modern Language Association (MLA)
Niu, Dongxiao…[et al.]. Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network. Mathematical Problems in Engineering No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1073271
American Medical Association (AMA)
Niu, Dongxiao& Lu, Yan& Xu, Xiaomin& Li, Bingjie. Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1073271
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073271