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...

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Main Authors: Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono, Suhartono, Abdul Ghapor Hussin, Yong Zulina Zubairi
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2018
Online Access:http://journalarticle.ukm.my/12022/
http://journalarticle.ukm.my/12022/
http://journalarticle.ukm.my/12022/1/UKM%20SAINSMalaysiana%2047%2802%29Feb%202018%2025.pdf
id ukm-12022
recordtype eprints
spelling ukm-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
repository_type Digital Repository
institution_category Local University
institution Universiti Kebangasaan Malaysia
building UKM Institutional Repository
collection Online Access
language English
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.
format Article
author Nur Arina Bazilah Kamisan,
Muhammad Hisyam Lee,
Suhartono, Suhartono
Abdul Ghapor Hussin,
Yong Zulina Zubairi,
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
author_facet Nur Arina Bazilah Kamisan,
Muhammad Hisyam Lee,
Suhartono, Suhartono
Abdul Ghapor Hussin,
Yong Zulina Zubairi,
author_sort Nur Arina Bazilah Kamisan,
title 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_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_sort load forecasting using combination model of multiple linear regression with neural network for malaysian city
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2018
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
first_indexed 2023-09-18T20:01:41Z
last_indexed 2023-09-18T20:01:41Z
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