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Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models

Author

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  • Warut Pannakkong

    (School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Klong Luang 12121, Pathum Thani, Thailand)

  • Thanyaporn Harncharnchai

    (School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Klong Luang 12121, Pathum Thani, Thailand)

  • Jirachai Buddhakulsomsiri

    (School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Klong Luang 12121, Pathum Thani, Thailand)

Abstract

This article involves forecasting daily electricity consumption in Thailand. Electricity consumption data are provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Five forecasting techniques, including multiple linear regression, artificial neural network (ANN), support vector machine, hybrid models, and ensemble models, are implemented. The article proposes a hyperparameter tuning technique, called sequential grid search, which is based on the widely used grid search, for ANN and hybrid models. Auxiliary variables and indicator variables that can improve the models’ forecasting performance are included. From the computational experiment, the hybrid model of a multiple regression model to forecast the expected daily consumption and ANNs from the sequential grid search to forecast the error term, along with additional indicator variables for some national holidays, provides the best mean absolution percentage error of 1.5664% on the test data set.

Suggested Citation

  • Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3105-:d:800972
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    References listed on IDEAS

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    Cited by:

    1. Maria Christina Gudrun Hart & Michael Hans Breitner, 2022. "Fostering Energy Resilience in the Rural Thai Power System—A Case Study in Nakhon Phanom," Energies, MDPI, vol. 15(19), pages 1-20, October.
    2. Katarzyna Poczeta & Elpiniki I. Papageorgiou, 2022. "Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks," Energies, MDPI, vol. 15(20), pages 1-18, October.
    3. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    4. 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.
    5. Xiao Li & Yu Zhang & Jing Liu & Zuomeng Sun, 2023. "Towards Sustainable Energy–Water–Environment Nexus System Considering the Interactions between Climatic, Social and Economic Factors: A Case Study of Fujian, China," Sustainability, MDPI, vol. 15(12), pages 1-22, June.

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