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: | 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 |
Similar Items
-
JMASM39: Algorithm for combining robust and bootstrap in multiple linear model regression
by: Wan Ahmad, Wan Muhamad Amir, et al.
Published: (2016) -
Deep neural network for forecasting inflow and outflow in Indonesia
by: Suhartono,, et al.
Published: (2019) -
A complex linear regression model
by: Abdul Ghapor Hussin,, et al.
Published: (2010) -
A weighted Fuzzy time series model for forecasting seasonal data(Suatu model siri masakabur berpemberat untuk meramal data bermusim)
by: Muhammad Hisyam Lee,, et al.
Published: (2012) -
Comparison between fuzzy bootstrap weighted multiple linear regression and
multiple linear regression: a case study for oral cancer modelling
by: Mohd Ibrahim, Mohamad Shafiq, et al.
Published: (2018)