Sequential process of feature extraction methods for artificial neural network in short term load forecasting
The first stage of feature extraction involves a transformation of raw data that is from the chronological hourly peak loads to the multiple time lags of hourly peak loads. This is followed by the next feature extraction wherein the principal component analysis (PCA) is used to further improve the i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Asian Research Publishing Network (ARPN)
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/46265/ http://psasir.upm.edu.my/id/eprint/46265/1/Sequential%20process%20of%20feature%20extraction%20methods%20for%20artificial%20neural%20network%20in%20short%20term%20load%20forecasting.pdf |
| _version_ | 1848850627302522880 |
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| author | Othman, Muhammad Murtadha Harun, Mohd Hafez Hilmi Salim, Nur Ashida Othman, Mohammad Lutfi |
| author_facet | Othman, Muhammad Murtadha Harun, Mohd Hafez Hilmi Salim, Nur Ashida Othman, Mohammad Lutfi |
| author_sort | Othman, Muhammad Murtadha |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The first stage of feature extraction involves a transformation of raw data that is from the chronological hourly peak loads to the multiple time lags of hourly peak loads. This is followed by the next feature extraction wherein the principal component analysis (PCA) is used to further improve the input data which will significantly enhance the performance of ANN in forecasting the hourly peak loads with less error. The output of ANN is then converted to a non-stationary form which represents as the forecasted hourly peak load for the next 24 hour. The Malaysian hourly peak loads in the year 2002 is used as case study to verify the effectiveness of ANN in STLF. |
| first_indexed | 2025-11-15T10:09:18Z |
| format | Article |
| id | upm-46265 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:09:18Z |
| publishDate | 2015 |
| publisher | Asian Research Publishing Network (ARPN) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-462652022-06-17T02:00:21Z http://psasir.upm.edu.my/id/eprint/46265/ Sequential process of feature extraction methods for artificial neural network in short term load forecasting Othman, Muhammad Murtadha Harun, Mohd Hafez Hilmi Salim, Nur Ashida Othman, Mohammad Lutfi The first stage of feature extraction involves a transformation of raw data that is from the chronological hourly peak loads to the multiple time lags of hourly peak loads. This is followed by the next feature extraction wherein the principal component analysis (PCA) is used to further improve the input data which will significantly enhance the performance of ANN in forecasting the hourly peak loads with less error. The output of ANN is then converted to a non-stationary form which represents as the forecasted hourly peak load for the next 24 hour. The Malaysian hourly peak loads in the year 2002 is used as case study to verify the effectiveness of ANN in STLF. Asian Research Publishing Network (ARPN) 2015-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/46265/1/Sequential%20process%20of%20feature%20extraction%20methods%20for%20artificial%20neural%20network%20in%20short%20term%20load%20forecasting.pdf Othman, Muhammad Murtadha and Harun, Mohd Hafez Hilmi and Salim, Nur Ashida and Othman, Mohammad Lutfi (2015) Sequential process of feature extraction methods for artificial neural network in short term load forecasting. ARPN Journal of Engineering and Applied Sciences, 10 (19). pp. 8830-8838. ISSN 1819-6608 http://www.arpnjournals.com/jeas/volume_19_2015.htm |
| spellingShingle | Othman, Muhammad Murtadha Harun, Mohd Hafez Hilmi Salim, Nur Ashida Othman, Mohammad Lutfi Sequential process of feature extraction methods for artificial neural network in short term load forecasting |
| title | Sequential process of feature extraction methods for artificial neural network in short term load forecasting |
| title_full | Sequential process of feature extraction methods for artificial neural network in short term load forecasting |
| title_fullStr | Sequential process of feature extraction methods for artificial neural network in short term load forecasting |
| title_full_unstemmed | Sequential process of feature extraction methods for artificial neural network in short term load forecasting |
| title_short | Sequential process of feature extraction methods for artificial neural network in short term load forecasting |
| title_sort | sequential process of feature extraction methods for artificial neural network in short term load forecasting |
| url | http://psasir.upm.edu.my/id/eprint/46265/ http://psasir.upm.edu.my/id/eprint/46265/ http://psasir.upm.edu.my/id/eprint/46265/1/Sequential%20process%20of%20feature%20extraction%20methods%20for%20artificial%20neural%20network%20in%20short%20term%20load%20forecasting.pdf |