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Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model

This study proposed a weighted fractional grey model (WFGM) based on a genetic algorithm for forecasting annual electricity consumption. WFGM has two parameters that can be used to adjust the order of the summation based on different data sequences and reflect the new information priority. The key i...

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Main Authors: Shabri, Ani, Samsudin, Ruhaidah, Alromema, Waseem
Format: Book Section
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
HD30.2 Knowledge management
Online Access:http://eprints.utm.my/id/eprint/99695/
http://dx.doi.org/10.1007/978-3-030-98741-1_6
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spelling my.utm.996952023-04-04T07:03:22Z http://eprints.utm.my/id/eprint/99695/ Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model Shabri, Ani Samsudin, Ruhaidah Alromema, Waseem HD30.2 Knowledge management This study proposed a weighted fractional grey model (WFGM) based on a genetic algorithm for forecasting annual electricity consumption. WFGM has two parameters that can be used to adjust the order of the summation based on different data sequences and reflect the new information priority. The key issue with the WFGM model is determining two optimum fractional-order values to improve the accuracy of electricity consumption forecasts. The Genetic Algorithm (GA) is used to select the best values for the weighted fractional-order accumulation, which is one of the most important aspects determining the grey model's prediction accuracy. The additional linear parameters of grey models are estimated using the least squares estimation method. Finally, two real data sets of electricity consumption from Malaysia and Thailand are presented to validate the proposed model. Numerical results show that the new proposed prediction model is very efficient and has the best prediction accuracy compared to the models of GM(1,1) and FGM(1,1). Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Shabri, Ani and Samsudin, Ruhaidah and Alromema, Waseem (2022) Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model. In: Advances on Intelligent Informatics and Computing Health Informatics, Intelligent Systems, Data Science and Smart Computing. Lecture Notes on Data Engineering and Communications Technologies, 127 (NA). Springer Science and Business Media Deutschland GmbH, Cham, Switzerland, pp. 62-72. ISBN 978-3-030-98740-4 http://dx.doi.org/10.1007/978-3-030-98741-1_6 DOI : 10.1007/978-3-030-98741-1_6
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 HD30.2 Knowledge management
spellingShingle HD30.2 Knowledge management
Shabri, Ani
Samsudin, Ruhaidah
Alromema, Waseem
Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model
description This study proposed a weighted fractional grey model (WFGM) based on a genetic algorithm for forecasting annual electricity consumption. WFGM has two parameters that can be used to adjust the order of the summation based on different data sequences and reflect the new information priority. The key issue with the WFGM model is determining two optimum fractional-order values to improve the accuracy of electricity consumption forecasts. The Genetic Algorithm (GA) is used to select the best values for the weighted fractional-order accumulation, which is one of the most important aspects determining the grey model's prediction accuracy. The additional linear parameters of grey models are estimated using the least squares estimation method. Finally, two real data sets of electricity consumption from Malaysia and Thailand are presented to validate the proposed model. Numerical results show that the new proposed prediction model is very efficient and has the best prediction accuracy compared to the models of GM(1,1) and FGM(1,1).
format Book Section
author Shabri, Ani
Samsudin, Ruhaidah
Alromema, Waseem
author_facet Shabri, Ani
Samsudin, Ruhaidah
Alromema, Waseem
author_sort Shabri, Ani
title Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model
title_short Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model
title_full Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model
title_fullStr Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model
title_full_unstemmed Improve short-term electricity consumption forecasting using a GA-based weighted fractional grey model
title_sort improve short-term electricity consumption forecasting using a ga-based weighted fractional grey model
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://eprints.utm.my/id/eprint/99695/
http://dx.doi.org/10.1007/978-3-030-98741-1_6
_version_ 1762837427757514752
score 13.18916

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