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Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network

Shukur, Osamah Basheer and Fadhil, Naam Salem and Lee, Muhammad Hisyam and Ahmad, Maizah Hura (2014) Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network. Jurnal Teknologi (Sciences and Engineering), 69 (2). pp. 65-70. ISSN 2180-3722

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Official URL: https://dx.doi.org/10.11113/jt.v69.3109

Abstract

Electricity load forecasting often has many properties such as the nonlinearity, double seasonal cycles, and others those may be obstacles for the accuracy of forecasting using some classical statistical models. Many papers in this field have proposed using double seasonal (DS) exponential smoothing model to forecast. These papers indicated that electricity load forecasting using DS exponential smoothing model has better fit. Using artificial neural network (ANN) as a modern approach may be used for superior fitted forecasting, since this approach can deal with the non-linearity components of load data. The purpose of this paper is to improve the electricity load forecasting by building the hybrid model that includes a double seasonal exponential smoothing with an artificial neural network. This hybrid model will study the double seasonal effects and non-linearity components together based on the electricity load data. The strategy of building this hybrid model is by entering ANN output as an input in double seasonal exponential smoothing model. The data sets are taken from three stations with different electricity load characteristics such as a residential, industrial and city center. The electricity load testing forecast of DS exponential smoothing-ANN hybrid model gave the most minimum mean absolute percentage error (MAPE) measurement comparing with the electricity load testing forecasts of DS exponential smoothing and ANN for all electricity load data sets. In conclusion, DS exponential smoothing-ANN hybrid model are the most fitted for every electricity load data which contains the double seasonal effects and non-linearity components.

Item Type:Article
Uncontrolled Keywords:electricity load forecasting, multiplicative double seasonal exponential smoothing, ANN, hybrid model
Subjects:Q Science
Divisions:Science
ID Code:52643
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 11:53
Last Modified:30 Jun 2018 06:45

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