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Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models

Author

Listed:
  • Hossein Iranmanesh

    (Department of Industrial Engineering, University of Tehran, Tehran, Iran
    Institute for International Energy Studies, Tehran, Iran)

  • Majid Abdollahzade

    (Institute for International Energy Studies, Tehran, Iran
    Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis New City, Tehran, Iran)

  • Arash Miranian

    (Institute for International Energy Studies, Tehran, Iran
    Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis New City, Tehran, Iran)

Abstract

This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT) learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.

Suggested Citation

  • Hossein Iranmanesh & Majid Abdollahzade & Arash Miranian, 2011. "Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models," Energies, MDPI, vol. 5(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:5:y:2011:i:1:p:1-21:d:15373
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    References listed on IDEAS

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    1. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    2. Mise, Emi & Kim, Tae-Hwan & Newbold, Paul, 2005. "On suboptimality of the Hodrick-Prescott filter at time series endpoints," Journal of Macroeconomics, Elsevier, vol. 27(1), pages 53-67, March.
    3. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    4. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    5. Morten O. Ravn & Harald Uhlig, 2002. "On adjusting the Hodrick-Prescott filter for the frequency of observations," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 371-375.
    6. Azadeh, A. & Khakestani, M. & Saberi, M., 2009. "A flexible fuzzy regression algorithm for forecasting oil consumption estimation," Energy Policy, Elsevier, vol. 37(12), pages 5567-5579, December.
    7. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    8. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    9. 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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