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Predicting monthly biofuel production using a hybrid ensemble forecasting methodology

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  • Yu, Lean
  • Liang, Shaodong
  • Chen, Rongda
  • Lai, Kin Keung

Abstract

This paper proposes a hybrid ensemble forecasting methodology that integrating empirical mode decomposition (EMD), long short-term memory (LSTM) and extreme learning machine (ELM) for the monthly biofuel (a typical agriculture-related energy) production based on the principle of decomposition—reconstruction—ensemble. The proposed methodology involves four main steps: data decomposition via EMD, component reconstruction via a fine-to-coarse (FTC) method, individual prediction via LSTM and ELM algorithms, and ensemble prediction via a simple addition (ADD) method. For illustration and verification, the biofuel monthly production data of the USA is used as the our sample data, and the empirical results indicate that the proposed hybrid ensemble forecasting model statistically outperforms all considered benchmark models considered in terms of the forecasting accuracy. This indicates that the proposed hybrid ensemble forecasting methodology integrating the EMD-LSTM-ELM models based on the decomposition—reconstruction—ensemble principle has been proved to be a competitive model for the prediction of biofuel production.

Suggested Citation

  • Yu, Lean & Liang, Shaodong & Chen, Rongda & Lai, Kin Keung, 2022. "Predicting monthly biofuel production using a hybrid ensemble forecasting methodology," International Journal of Forecasting, Elsevier, vol. 38(1), pages 3-20.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:1:p:3-20
    DOI: 10.1016/j.ijforecast.2019.08.014
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    References listed on IDEAS

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    2. Jeonghwa Cha & Kyungbo Park & Hangook Kim & Jongyi Hong, 2023. "Crisis Index Prediction Based on Momentum Theory and Earnings Downside Risk Theory: Focusing on South Korea’s Energy Industry," Energies, MDPI, vol. 16(5), pages 1-20, February.
    3. Xu, Kunliang & Niu, Hongli, 2022. "Do EEMD based decomposition-ensemble models indeed improve prediction for crude oil futures prices?," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    4. Patience Afi Seglah & Yajing Wang & Hongyan Wang & Chunyu Gao & Yuyun Bi, 2022. "Sustainable Biofuel Production from Animal Manure and Crop Residues in Ghana," Energies, MDPI, vol. 15(16), pages 1-17, August.
    5. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    6. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    7. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
    8. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).

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