Universiti Teknologi Malaysia Institutional Repository

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

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

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Official URL: http://dx.doi.org/10.1007/978-3-030-98741-1_6


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).

Item Type:Book Section
Uncontrolled Keywords:First grey model, Forecasting, Genetic algorithm, Weighted fractional
Subjects:H Social Sciences > HD Industries. Land use. Labor > HD30.2 Knowledge management
ID Code:99695
Deposited By: Widya Wahid
Deposited On:10 Mar 2023 09:44
Last Modified:04 Apr 2023 15:03

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