The Taguchi-neural networks approach to forecast electricity consumption
Neural networks (NN) have been widely used for electricity forecasting, but some difficulties are still found. One of those difficulties is in choosing the optimal network parameter, which are strongly important to obtain accurate result. "Trial and error" commonly used to set the paramete...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2008
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| Subjects: | |
| Online Access: | http://scholars.utp.edu.my/id/eprint/466/ http://scholars.utp.edu.my/id/eprint/466/1/paper.pdf |
| _version_ | 1848658994105679872 |
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| author | M.F., Romlie D., Purwanto H., Agustiawan |
| author_facet | M.F., Romlie D., Purwanto H., Agustiawan |
| author_sort | M.F., Romlie |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | Neural networks (NN) have been widely used for electricity forecasting, but some difficulties are still found. One of those difficulties is in choosing the optimal network parameter, which are strongly important to obtain accurate result. "Trial and error" commonly used to set the parameter is ineffective in terms of processing time and the accuracy. In this paper, Taguchi method is employed to optimize the accuracy of NN based prediction. This hybrid approach results in the optimal network parameters. Those are: 1 for the history length, 1 day for sampling time, and 8 nodes for hidden neurons. The method is used to predict electricity consumption in Universiti Teknologi PETRONAS (UTP), Malaysia. From the preliminary results it is found that the combined method seems to be a convincing approach. © 2008 IEEE.
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| first_indexed | 2025-11-13T07:23:22Z |
| format | Conference or Workshop Item |
| id | oai:scholars.utp.edu.my:466 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:23:22Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:4662017-01-19T08:26:34Z http://scholars.utp.edu.my/id/eprint/466/ The Taguchi-neural networks approach to forecast electricity consumption M.F., Romlie D., Purwanto H., Agustiawan TK Electrical engineering. Electronics Nuclear engineering Neural networks (NN) have been widely used for electricity forecasting, but some difficulties are still found. One of those difficulties is in choosing the optimal network parameter, which are strongly important to obtain accurate result. "Trial and error" commonly used to set the parameter is ineffective in terms of processing time and the accuracy. In this paper, Taguchi method is employed to optimize the accuracy of NN based prediction. This hybrid approach results in the optimal network parameters. Those are: 1 for the history length, 1 day for sampling time, and 8 nodes for hidden neurons. The method is used to predict electricity consumption in Universiti Teknologi PETRONAS (UTP), Malaysia. From the preliminary results it is found that the combined method seems to be a convincing approach. © 2008 IEEE. 2008 Conference or Workshop Item NonPeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/466/1/paper.pdf M.F., Romlie and D., Purwanto and H., Agustiawan (2008) The Taguchi-neural networks approach to forecast electricity consumption. In: IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2008, 4 May 2008 through 7 May 2008, Niagara Falls, ON. http://www.scopus.com/inward/record.url?eid=2-s2.0-51849155634&partnerID=40&md5=a50bb783cfadc77aca6d020dcbf0b0f5 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering M.F., Romlie D., Purwanto H., Agustiawan The Taguchi-neural networks approach to forecast electricity consumption |
| title | The Taguchi-neural networks approach to forecast electricity consumption
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| title_full | The Taguchi-neural networks approach to forecast electricity consumption
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| title_fullStr | The Taguchi-neural networks approach to forecast electricity consumption
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| title_full_unstemmed | The Taguchi-neural networks approach to forecast electricity consumption
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| title_short | The Taguchi-neural networks approach to forecast electricity consumption
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| title_sort | taguchi-neural networks approach to forecast electricity consumption |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://scholars.utp.edu.my/id/eprint/466/ http://scholars.utp.edu.my/id/eprint/466/ http://scholars.utp.edu.my/id/eprint/466/1/paper.pdf |