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Electricity Consumption Forecasting in Thailand using Hybrid Model SARIMA and Gaussian Process with Combine Kernel Function Technique

Author

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  • Poonpong Suksawang

    (College of Research Methodology and Cognitive Science, Burapha University, Chonburi, 20131, Thailand,)

  • Sukonthip Suphachan

    (College of Research Methodology and Cognitive Science, Burapha University, Chonburi, 20131, Thailand,)

  • Kanokkarn Kaewnuch

    (National Institute of Development Administration, Bangkok, Thailand)

Abstract

Electricity consumption forecasting plays a significant role in planning electric systems. However, this can only be achieved if the demand is accurate estimation .This research, different forecasting methods hybrid SARIMA-ANN and hybrid model by SARIMA- Gaussian Processes with combine Kernel Function technique were utilized to formulate forecasting models of the electricity consumption . The objective was to compare the performance of two approaches and the empirical data used in this study was the historical data regarding the electricity consumption (gross domestic product: GDP, forecast values calculated by SARIMA model and electricity consumption) in Thailand from 2005 to 2015. New Kernel Function design techniques for forecasting under Gaussian processes are presented in sum and product formats. The results showed that the hybrid model by SARIMA - Gaussian Processes with combine Kernel Function technique outperformed the SARIMA-ANN model have the Mean absolute percentage error is 4.7072e-09, 4.8623 respectively.

Suggested Citation

  • Poonpong Suksawang & Sukonthip Suphachan & Kanokkarn Kaewnuch, 2018. "Electricity Consumption Forecasting in Thailand using Hybrid Model SARIMA and Gaussian Process with Combine Kernel Function Technique," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 98-109.
    • Handle: RePEc:eco:journ2:2018-04-13
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    References listed on IDEAS

    as
    1. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
    2. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    3. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
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    Cited by:

    1. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    2. Paryono Paryono & Khudzaifah Dimyati & Absori Absori & Shinta Dewi Rismawati, 2019. "The Hegemony of Global Capitalism in the Regulation of Electricity: The Electricity Policies of the Selected Southeast Asian Nations," International Journal of Energy Economics and Policy, Econjournals, vol. 9(6), pages 326-335.
    3. Ademola Abdulkareem & E. J. Okoroafor & Ayokunle Awelewa & Aderibigbe Adekitan, 2019. "Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University s Experience," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 103-109.

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    More about this item

    Keywords

    Forecasting ; Electricity Consumption ; Model ; Gaussian Process;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment

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