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2016•Unpublished venueRequires access

Comparative Performance Of ARIMA And DES Models In Forecasting Electricity Load Demand In Malaysia

Miswan Nor Hamizah, Mohd Said Rahaini, Hussin Nor Hafizah, Khairum Hamzah, Emy Zairah Ahmad

Open publisher page 7 citations

Abstract

Malaysia is a developing country which is having a high level of energy demand. Load demand forecasting is essential that is also in line with increasing demand of electricity. The purpose of the current study is to compare the performance of two time series models in forecasting electricity load demand in Malaysia. Two methods are considered, which are Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES). Using Mean Absolute Percentage Error (MAPE) as the forecasting performance measure, the study concludes that ARIMA is more appropriate model.

About this research paper

What this paper is about

Malaysia is a developing country which is having a high level of energy demand. Load demand forecasting is essential that is also in line with increasing demand of electricity. The purpose of the current study is to compare the performance of two time series models in forecasting electricity load demand in Malaysia. Two methods are considered, which are Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES). Using Mean Absolute Percentage Error (MAPE) as the forecasting performance measure, the study concludes that ARIMA is more appropriate model.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Malaysia is a developing country which is having a high level of energy demand. Load demand forecasting is essential that is also in line with increasing demand of electricity. The purpose of the current study is to compare the performance of two time series models in forecasting electricity load demand in Malaysia. Two methods are considered, which are Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES). Using Mean Absolute Percentage Error (MAPE) as the forecasting performance measure, the study concludes that ARIMA is more appropriate model.

Key concepts: Exponential smoothing, Autoregressive integrated moving average, Demand forecasting, Mean absolute percentage error, Econometrics, Electricity, Electricity demand, Time series

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