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A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting

Author

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  • Lalitpat Aswanuwath

    (School of Manufacturing Systems and Mechanical Engineering (MSME), Sirindhorn International Institute of Technology (SIIT), Thammasat University, 99 Moo 18, Paholyothin Road, Pathum Thani 12120, Thailand
    School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan)

  • Warut Pannakkong

    (School of Manufacturing Systems and Mechanical Engineering (MSME), Sirindhorn International Institute of Technology (SIIT), Thammasat University, 99 Moo 18, Paholyothin Road, Pathum Thani 12120, Thailand)

  • Jirachai Buddhakulsomsiri

    (School of Manufacturing Systems and Mechanical Engineering (MSME), Sirindhorn International Institute of Technology (SIIT), Thammasat University, 99 Moo 18, Paholyothin Road, Pathum Thani 12120, Thailand)

  • Jessada Karnjana

    (National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), 112 Thailand Science Park (TSP), Paholyothin Road, Pathum Thani 12120, Thailand)

  • Van-Nam Huynh

    (School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan)

Abstract

Daily electricity peak load forecasting is important for electricity generation capacity planning. Accurate forecasting leads to saving on excessive electricity generating capacity, while maintaining the stability of the power system. The main challenging tasks in this research field include improving forecasting accuracy and reducing computational time. This paper proposes a hybrid model involving variational mode decomposition (VMD), empirical mode decomposition (EMD), fast Fourier transform (FFT), stepwise regression, similar days selection (SD) method, and artificial neural network (ANN) for daily electricity peak load forecasting. Stepwise regression and similar days selection method are used for input variable selection. VMD and FFT are applied for data decomposition and seasonality capturing, while EMD is employed for determining an appropriate decomposition level for VMD. The hybrid model is constructed to effectively forecast special holidays, which have different patterns from other normal weekdays and weekends. The performance of the hybrid model is tested with real electricity peak load data provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Experimental results show that the hybrid model gives the best performance while saving computation time by solving the problems in input variable selection, data decomposition, and imbalance data of normal and special days in the training process.

Suggested Citation

  • Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1860-:d:1067428
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    References listed on IDEAS

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    Cited by:

    1. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand," Energies, MDPI, vol. 17(1), pages 1-31, December.
    2. Mustafa Saglam & Xiaojing Lv & Catalina Spataru & Omer Ali Karaman, 2024. "Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning," Energies, MDPI, vol. 17(4), pages 1-22, February.

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