Load forecasting using combination model of multiple linear regression with neural network for Malaysian City
Document Type
Article
Publication Date
1-1-2018
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
Divisions
mathematics
Publication Title
Sains Malaysiana
Volume
47
Issue
2
Publisher
Penerbit Universiti Kebangsaan Malaysia