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, (Penerbit Universiti Kebangsaan Malaysia, 2018)
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.