IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2018-04-13.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/6571/3791
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijeep/article/view/6571/3791
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. Çağlayan-Akay, Ebru & Topal, Kadriye Hilal, 2024. "Forecasting Turkish electricity consumption: A critical analysis of single and hybrid models," Energy, Elsevier, vol. 305(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    2. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    3. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
    4. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    5. Ricardo Vazquez & Hortensia Amaris & Monica Alonso & Gregorio Lopez & Jose Ignacio Moreno & Daniel Olmeda & Javier Coca, 2017. "Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project," Energies, MDPI, vol. 10(2), pages 1-23, February.
    6. Zhang, Yue & Farnoosh, Arash, 2019. "Analyzing the dynamic impact of electricity futures on revenue and risk of renewable energy in China," Energy Policy, Elsevier, vol. 132(C), pages 678-690.
    7. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
    8. Shi, Yong & Ren, Xinyue & Guo, Kun & Zhou, Yi & Wang, Jun, 2020. "Research on the economic development pattern of Chinese counties based on electricity consumption," Energy Policy, Elsevier, vol. 147(C).
    9. Wang, Lei & Wang, Xinyu & Zhao, Zhongchao, 2024. "Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression," Energy, Elsevier, vol. 304(C).
    10. Ghaem Sigarchian, Sara & Paleta, Rita & Malmquist, Anders & Pina, André, 2015. "Feasibility study of using a biogas engine as backup in a decentralized hybrid (PV/wind/battery) power generation system – Case study Kenya," Energy, Elsevier, vol. 90(P2), pages 1830-1841.
    11. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
    12. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    13. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
    14. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, vol. 11(8), pages 1-18, August.
    15. Jaka Rober & Leon Maruša & Miloš Beković, 2023. "A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers," Energies, MDPI, vol. 16(17), pages 1-20, August.
    16. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    17. Yujia Ge & Yurong Nan & Lijun Bai, 2019. "A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks," Energies, MDPI, vol. 12(24), pages 1-21, December.
    18. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    19. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    20. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ2:2018-04-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.