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

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...

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Main Authors: Shukur, Osamah Basheer, Fadhil, Naam Salem, Lee, Muhammad Hisyam, Ahmad, Maizah Hura
Format: Article
Published: Penerbit UTM 2014
Subjects:
Q Science
Online Access:http://eprints.utm.my/id/eprint/52643/
https://dx.doi.org/10.11113/jt.v69.3109
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spelling my.utm.526432018-06-29T22:45:48Z http://eprints.utm.my/id/eprint/52643/ Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network Shukur, Osamah Basheer Fadhil, Naam Salem Lee, Muhammad Hisyam Ahmad, Maizah Hura Q Science 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. Penerbit UTM 2014 Article PeerReviewed 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 https://dx.doi.org/10.11113/jt.v69.3109 DOI:10.11113/jt.v69.3109
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science
spellingShingle Q Science
Shukur, Osamah Basheer
Fadhil, Naam Salem
Lee, Muhammad Hisyam
Ahmad, Maizah Hura
Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network
description 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.
format Article
author Shukur, Osamah Basheer
Fadhil, Naam Salem
Lee, Muhammad Hisyam
Ahmad, Maizah Hura
author_facet Shukur, Osamah Basheer
Fadhil, Naam Salem
Lee, Muhammad Hisyam
Ahmad, Maizah Hura
author_sort Shukur, Osamah Basheer
title Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network
title_short Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network
title_full Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network
title_fullStr Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network
title_full_unstemmed Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network
title_sort electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network
publisher Penerbit UTM
publishDate 2014
url http://eprints.utm.my/id/eprint/52643/
https://dx.doi.org/10.11113/jt.v69.3109
_version_ 1643653217812217856
score 13.211869

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