Load forecasting using combination model of multiple linear regression with neural network for Malaysian city
Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the forecast a linear relationship with other factors but MLR ha...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2018
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| Online Access: | http://journalarticle.ukm.my/12022/ http://journalarticle.ukm.my/12022/1/UKM%20SAINSMalaysiana%2047%2802%29Feb%202018%2025.pdf |
| _version_ | 1848812888541626368 |
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| author | Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono, Suhartono Abdul Ghapor Hussin, Yong Zulina Zubairi, |
| author_facet | Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono, Suhartono Abdul Ghapor Hussin, Yong Zulina Zubairi, |
| author_sort | Nur Arina Bazilah Kamisan, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
error graphically. From the result obtained this model gives a better forecast compare to the other two models. |
| first_indexed | 2025-11-15T00:09:27Z |
| format | Article |
| id | oai:generic.eprints.org:12022 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:09:27Z |
| publishDate | 2018 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:120222018-08-21T07:26:38Z http://journalarticle.ukm.my/12022/ Load forecasting using combination model of multiple linear regression with neural network for Malaysian city Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono, Suhartono Abdul Ghapor Hussin, Yong Zulina Zubairi, Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the error graphically. From the result obtained this model gives a better forecast compare to the other two models. Penerbit Universiti Kebangsaan Malaysia 2018-02 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/12022/1/UKM%20SAINSMalaysiana%2047%2802%29Feb%202018%2025.pdf Nur Arina Bazilah Kamisan, and Muhammad Hisyam Lee, and Suhartono, Suhartono and Abdul Ghapor Hussin, and Yong Zulina Zubairi, (2018) Load forecasting using combination model of multiple linear regression with neural network for Malaysian city. Sains Malaysiana, 47 (2). pp. 419-426. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol47num2_2018/contentsVol47num2_2018.html |
| spellingShingle | Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono, Suhartono Abdul Ghapor Hussin, Yong Zulina Zubairi, Load forecasting using combination model of multiple linear regression with neural network for Malaysian city |
| title | Load forecasting using combination model of multiple linear regression with neural network for Malaysian city |
| title_full | Load forecasting using combination model of multiple linear regression with neural network for Malaysian city |
| title_fullStr | Load forecasting using combination model of multiple linear regression with neural network for Malaysian city |
| title_full_unstemmed | Load forecasting using combination model of multiple linear regression with neural network for Malaysian city |
| title_short | Load forecasting using combination model of multiple linear regression with neural network for Malaysian city |
| title_sort | load forecasting using combination model of multiple linear regression with neural network for malaysian city |
| url | http://journalarticle.ukm.my/12022/ http://journalarticle.ukm.my/12022/ http://journalarticle.ukm.my/12022/1/UKM%20SAINSMalaysiana%2047%2802%29Feb%202018%2025.pdf |