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Improve Short-term Electricity Consumption Forecasting Using a GA-Based Weighted Fractional Grey Model

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Advances on Intelligent Informatics and Computing (IRICT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 127))

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Abstract

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).

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Acknowledgments

The authors are grateful to the Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia for fully supporting and funding this research project through the Fundamental Research Grant Scheme under Grant Vot 5F271.

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Authors and Affiliations

  1. Faculty of Science, Universiti Teknologi Malaysia, 81300, Johor Bahru, Johor, Malaysia

    Ani Shabri

  2. Faculty of Engineering, Universiti Teknologi Malaysia, 81300, Johor Bahru, Johor, Malaysia

    Ruhaidah Samsudin

  3. Computer Science and Information Systems, Applied College, Taibah University, Medina, Saudi Arabia

    Waseem Alromema

Authors
  1. Ani Shabri
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  2. Ruhaidah Samsudin
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  3. Waseem Alromema
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Corresponding author

Correspondence to Ani Shabri .

Editor information

Editors and Affiliations

  1. Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah, Malaysia

    Fathey Mohammed

  3. Department of Computer Science, School of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Fuad Ghaleb

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Shabri, A., Samsudin, R., Alromema, W. (2022). Improve Short-term Electricity Consumption Forecasting Using a GA-Based Weighted Fractional Grey Model. In: Saeed, F., Mohammed, F., Ghaleb, F. (eds) Advances on Intelligent Informatics and Computing. IRICT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-98741-1_6

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