Universiti Teknologi Malaysia Institutional Repository

Forecasting Malaysia load using a hybrid model

Mohamad, Norizan and Ahmad, Maizah Hura (2010) Forecasting Malaysia load using a hybrid model. Jurnal STATISTIKA, 10 (1). pp. 1-8. ISSN 1411-5891

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Abstract

A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feedforward neural network to forecast time series with seasonality, is shown to outperform both two single models. Besides the selection of transfer functions, the determination of hidden nodes to use for the non linear model is believed to improve the accuracy of the hybrid model. In this paper, we focus on the selection of the appropriate number of hidden nodes on the non linear model to forecast Malaysia load. Results show that by using only one hidden node, the hybrid model of Malaysia load performs better than both single models with mean absolute percentage error (MAPE) of less than 1%.

Item Type:Article
Uncontrolled Keywords:load forecasting, seasonal autoregressive integrated moving average, multilayer feedforward neural network, hybrid model, hidden nodes
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:25946
Deposited By: Liza Porijo
Deposited On:18 Jun 2012 03:23
Last Modified:27 Jan 2015 08:14

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