Load forecasting using combination model of multiple linear regression with neural network for Malaysian City

Kamisan, Nur Arina Bazilah and Lee, Muhammad Hisyam and Suhartono, Suhartono and Hussin, Abdul Ghapor and Zubairi, Yong Zulina (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

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Official URL: http://journalarticle.ukm.my/12022/1/UKM%20SAINSMa...

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.

Item Type: Article
Uncontrolled Keywords: Error plot; hybrid model; neural network; regression model; residuals
Subjects: Q Science > QA Mathematics
Divisions: Centre for Foundation Studies in Science > Mathematics Division
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 22 Jul 2019 07:05
Last Modified: 22 Jul 2019 07:05
URI: http://eprints.um.edu.my/id/eprint/21698

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