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Integrating heuristic time series with modified grey forecasting for renewable energy in Taiwan

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  • Lu, Shin-Li

Abstract

Incomplete or less data render traditional forecasting algorithms, such as regression analysis or time series analysis, unsuitable. Forecast based on less data is prone to errors because it fails to simulate data accurately. Therefore, this study integrates heuristic fuzzy time series with modified grey forecasting model, namely HFEGM(1,1), to improve the accuracy of the traditional GM(1,1) model for small datasets. Adopting the annual renewable energy in Taiwan, experimental results show that the HFEGM(1,1) model can effectively reduce forecasting errors of the HFGM(1,1) model and also enhance the forecasting accuracy of the GM(1,1) model. Particularly, the forecasting accuracy of the HFEGM(1,1) model for renewable energy is more than 90%, which can be used as a reference for formulating energy policy by managers.

Suggested Citation

  • Lu, Shin-Li, 2019. "Integrating heuristic time series with modified grey forecasting for renewable energy in Taiwan," Renewable Energy, Elsevier, vol. 133(C), pages 1436-1444.
    • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:1436-1444
    DOI: 10.1016/j.renene.2018.08.092
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    References listed on IDEAS

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    1. Chen, Falin & Lu, Shyi-Min & Wang, Eric & Tseng, Kuo-Tung, 2010. "Renewable energy in Taiwan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 2029-2038, September.
    2. Chen, Falin & Lu, Shyi-Min & Tseng, Kuo-Tung & Lee, Si-Chen & Wang, Eric, 2010. "Assessment of renewable energy reserves in Taiwan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2511-2528, December.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
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    Cited by:

    1. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh & Shaker, Hamid Reza, 2021. "High dimensional very short-term solar power forecasting based on a data-driven heuristic method," Energy, Elsevier, vol. 219(C).
    2. Maolin Cheng & Jiano Li & Yun Liu & Bin Liu, 2020. "Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).
    5. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).

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