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 accurate forecasting using some classical statistical models. Many papers in this field have proposed using double seasonal (DS) exponential smoothing model...

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Main Author:	Fadhil, Naam Salem
Format:	Thesis
Published:	2013
Subjects:	QC Physics
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id	my-utm-ep.42112
record_format	uketd_dc
spelling	my-utm-ep.421122020-08-05T02:06:10Z Electricity load forecasting using hybrid of multiplicative double seasonal exponential smoothing model with artificial neural network 2013 Fadhil, Naam Salem QC Physics Electricity load forecasting often has many properties such as the nonlinearity, double seasonal cycles, and others those may be obstacles for accurate forecasting using some classical statistical models. Many papers in this field have proposed using double seasonal (DS) exponential smoothing model to forecast. These were found that electricity load forecasting using DS exponential smoothing model will be fitted, since this model studies the double seasonal effects those are in the studied data. Using artificial neural network (ANN) as a modern approach may also enable for more fitted forecasting, since this approach can deal with the non-linearity components of load data .The purpose of this study is improving the electricity load forecasting by building the hybrid model which includes a double seasonal exponential smoothing with an artificial neural network .This hybrid model will be studied the double seasonal effects and non-linearity components together those are in 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 the city center .The electricity load testing forecast of DS exponential smoothing-ANN hybrid 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. 2013 Thesis http://eprints.utm.my/id/eprint/42112/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:81860 masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	QC Physics
spellingShingle	QC Physics Fadhil, Naam Salem 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 accurate forecasting using some classical statistical models. Many papers in this field have proposed using double seasonal (DS) exponential smoothing model to forecast. These were found that electricity load forecasting using DS exponential smoothing model will be fitted, since this model studies the double seasonal effects those are in the studied data. Using artificial neural network (ANN) as a modern approach may also enable for more fitted forecasting, since this approach can deal with the non-linearity components of load data .The purpose of this study is improving the electricity load forecasting by building the hybrid model which includes a double seasonal exponential smoothing with an artificial neural network .This hybrid model will be studied the double seasonal effects and non-linearity components together those are in 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 the city center .The electricity load testing forecast of DS exponential smoothing-ANN hybrid 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	Thesis
qualification_level	Master's degree
author	Fadhil, Naam Salem
author_facet	Fadhil, Naam Salem
author_sort	Fadhil, Naam Salem
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
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2013
_version_	1747816692672626688

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

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