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Fourier Residual Modified Approach in Group Method of Data Handling for Electricity Load Forecasting

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Advances in Intelligent Computing Techniques and Applications (IRICT 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 210))

Abstract

Electricity is a highly essential part of daily life, including industrial, commercial, residential, and other sectors. A strong developed nation has significantly impacted the rise of electricity load demand. Hence, accurate load forecasting is fundamental for future planning in various sectors, enabling organizations to make informed decisions, optimize resource utilization, and adapt to changing demands in a dynamic environment. Hence, this paper proposes a residual modification by integrating a Fourier Series residual modification technique on the fitted Autoregressive Integrated Moving Average (ARIMA) time series model and an Artificial Intelligence (AI) Group Method of Data Handling (GMDH) model named F-ARIMA and F-GMDH respectively. The outcomes are compared with the conventional ARIMA and GMDH single model using the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). The results reveal that F-GMDH outperforms the model without employing Fourier residual modification.

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Authors and Affiliations

  1. Faculty of Electrical Technology and Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Meleka, Malaysia

    Nur Rafiqah Abdul Razif

  2. Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    Nur Rafiqah Abdul Razif & Ani Shabri

Authors
  1. Nur Rafiqah Abdul Razif
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  2. Ani Shabri
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Correspondence to Nur Rafiqah Abdul Razif .

Editor information

Editors and Affiliations

  1. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. Department of Business Analytics, Sunway Business School, Sunway University, Selangor, Malaysia

    Fathey Mohammed

  3. Department of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV, USA

    Yousef Fazea

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Razif, N.R.A., Shabri, A. (2024). Fourier Residual Modified Approach in Group Method of Data Handling for Electricity Load Forecasting. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-031-59711-4_12

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