IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v5y2011i1p1-21d15373.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/5/1/1/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/5/1/1/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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.
    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. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.

    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. Konon, Alexander & Fritsch, Michael & Kritikos, Alexander S., 2018. "Business cycles and start-ups across industries: An empirical analysis of German regions," Journal of Business Venturing, Elsevier, vol. 33(6), pages 742-761.
    2. Alexander Konon & Michael Fritsch & Alexander Kritikos, 2017. "Business Cycles and Start-ups across Industries: an Empirical Analysis for Germany," Jena Economics Research Papers 2017-013, Friedrich-Schiller-University Jena.
    3. Adam Geršl & Thomas Mitterling, 2021. "Forecast-Augmented Credit-to-GDP Gap as an Early Warning Indicator of Banking Crises," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(4), pages 323-351, December.
    4. Harvey, Andrew C. & Delle Monache, Davide, 2009. "Computing the mean square error of unobserved components extracted by misspecified time series models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 283-295, February.
    5. Zsuzsanna Hosszu & Gergely Lakos, 2022. "Early Warning Performance of Univariate Credit-to-GDP Gaps," MNB Occasional Papers 2022/142, Magyar Nemzeti Bank (Central Bank of Hungary).
    6. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    7. KAFANDO, Namalguebzanga, 2014. "L'industrialisation de l'Afrique: l'importance des facteurs structurels et du régime de change [The industrialization of Africa: the importance of structural factors and exchange rate regime]," MPRA Paper 68736, University Library of Munich, Germany.
    8. Galimberti, Jaqueson K. & Moura, Marcelo L., 2016. "Improving the reliability of real-time output gap estimates using survey forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 358-373.
    9. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    10. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
    11. Habeebur Rahman & Iniyan Selvarasan & Jahitha Begum A, 2018. "Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach," Energies, MDPI, vol. 11(12), pages 1-21, December.
    12. Mayu Kikuchi & Alfred Wong & Jiayue Zhang, 2019. "Risk of window dressing: quarter-end spikes in the Japanese yen Libor-OIS spread," Journal of Regulatory Economics, Springer, vol. 56(2), pages 149-166, December.
    13. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    14. Maravall, A. & del Rio, A., 2007. "Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 975-998, October.
    15. Jacobs, Jan & Tassenaar, Vincent, 2004. "Height, income, and nutrition in the Netherlands: the second half of the 19th century," Economics & Human Biology, Elsevier, vol. 2(2), pages 181-195, June.
    16. Hartwig, Benny & Meinerding, Christoph & Schüler, Yves S., 2021. "Identifying indicators of systemic risk," Journal of International Economics, Elsevier, vol. 132(C).
    17. Carlos Mendez & Mitsuhiko Kataoka, 2021. "Disparities in regional productivity, capital accumulation, and efficiency across Indonesia: A club convergence approach," Review of Development Economics, Wiley Blackwell, vol. 25(2), pages 790-809, May.
    18. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    19. Sylviane Guillaumont Jeanneney & Sampawende Jules Tapsoba, 2011. "Pro cyclicité de la politique budgétaire et surveillance multilatérale dans les unions monétaires africaines," CERDI Working papers halshs-00554337, HAL.
    20. Martínez, Juan Francisco & Oda, Daniel, 2021. "Characterization of the Chilean financial cycle, early warning indicators and implications for macro-prudential policies," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(1).

    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:gam:jeners:v:5:y:2011:i:1:p:1-21:d:15373. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.