Sains Malaysiana 47(2)(2018): 419-426

http://dx.doi.org/10.17576/jsm-2018-4702-25

 

Load Forecasting using Combination Model of Multiple Linear Regression with Neural Network for Malaysian City

(Peramalan Beban Menggunakan Model Gabungan bagi Regresi Linear Berganda dengan Rangkaian Neuron untuk Bandaraya di Malaysia)

 

Nur Arina Bazilah Kamisan1, Muhammad Hisyam Lee1*, Suhartono Suhartono2, Abdul Ghapor Hussin3 & Yong Zulina Zubairi4

1Fakulti Sains, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor Darul Takzim, Malaysia

 

2Jalan Raya ITS, Keputih, Sukolilo, Kota SBY, Jawa Timur 60111, Indonesia

 

3Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 57000 Kuala Lumpur, Malaysia

 

4Universiti Malaya, Jalan Universiti, 50603 Kuala Lumpur, Wilayah Persekutuan, Malaysia

 

Diserahkan: 1 Mei 2016/Diterima: 16 Ogos 2017

 

 

Abstract

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.

Keywords: Error plot; hybrid model; neural network; regression model; residuals

 

Abstrak

Peramalan berganda data bermusim adalah berbeza daripada peramalan data bermusim biasa kerana ia mengandungi lebih daripada satu kitaran dalam satu data. Model berganda regresi linear (MLR) telah digunakan secara meluas dalam ramalan beban kerana kegunaannya dalam meramalkan hubungan linear dengan faktor lain tetapi MLR mempunyai kelemahan iaitu mempunyai kesukaran dalam memodelkan hubungan linear antara pemboleh ubah dan faktor yang mempengaruhi. Model rangkaian neural (NN) di sisi lain, adalah model yang baik dalam pemodelan data linear. Oleh itu, dalam kajian ini gabungan MLR dan NN model dicadangkan gabungan ini untuk mengatasi masalah tersebut. Model hibrid ini kemudiannya dibandingkan dengan MLR dan NN model untuk melihat prestasi model hibrid. RMSE digunakan sebagai penunjuk prestasi dan plot ralat grafik diperkenalkan untuk melihat ralat secara grafik. Daripada keputusan yang diperoleh model ini memberikan ramalan yang lebih baik berbanding dengan dua model yang lain.

Kata kunci: Model hibrid; model regresi; plot ralat; rangkaian neural; sisa

 

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*Pengarang untuk surat-menyurat; email: mhl@utm.my

 

 

 

 

 
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