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Principal component regression with artificial neural network to improve prediction of electricity demand
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 1A(s) (31 Dec. 2016), pp.196-202, 7 p.
Publisher
Publication Date
2016-12-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Planning for electricity demand is a key factor for the success in the development of any countries.
Such success can only be achieved if the demand for electricity is predicted correctly and accurately.
This study introduces a new hybrid approach that combines Principle Component Regression (PCR) and Back-Propagation Neural Networks (BPNN) techniques in order to improve the accuracy of the electricity demand prediction rates.
The study includes 13 factors that related to electricity demand, and data for these factors have been collected in Malaysia.
The new combination (PCR-BPNN) starts to solve the problem of collinearity among the input dataset, and hence, the reliability of the results.
The work focuses also on the errors that recoded at that output stage of the electricity prediction models due to changes in the patterns of the input dataset.
The accuracy and reliability of the results have been improved through the new proposed model.
Validations have been achieved for the proposed model through comparing the value of three performance indicators of the PCR-BPNN with the performance rates of three major prediction models.
Results show the outperformance of the PCR-BPNN over the other types of the electricity prediction models
American Psychological Association (APA)
Ismail, Nur& Abd Allah, Syamnd. 2016. Principal component regression with artificial neural network to improve prediction of electricity demand. The International Arab Journal of Information Technology،Vol. 13, no. 1A(s), pp.196-202.
https://search.emarefa.net/detail/BIM-758304
Modern Language Association (MLA)
Ismail, Nur& Abd Allah, Syamnd. Principal component regression with artificial neural network to improve prediction of electricity demand. The International Arab Journal of Information Technology Vol. 13, no. 1A (2016), pp.196-202.
https://search.emarefa.net/detail/BIM-758304
American Medical Association (AMA)
Ismail, Nur& Abd Allah, Syamnd. Principal component regression with artificial neural network to improve prediction of electricity demand. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 1A(s), pp.196-202.
https://search.emarefa.net/detail/BIM-758304
Data Type
Journal Articles
Language
English
Notes
Includes appendices : p. 202
Record ID
BIM-758304