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Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model


  • Koutroumanidis, Theodoros
  • Ioannou, Konstantinos
  • Arabatzis, Garyfallos


Throughout history, energy resources have acquired a strategic significance for the economic growth and social welfare of any country. The large-scale oil crisis of 1973 coupled with various environmental protection issues, have led many countries to look for new, alternative energy sources. Biomass and fuelwood in particular, constitutes a major renewable energy source (RES) that can make a significant contribution, as a substitute for oil. This paper initially provides a description of the contribution of renewable energy sources to the production of electricity, and also examines the role of forests in the production of fuelwood in Greece. Following this, autoregressive integrated moving average (ARIMA) models, artificial neural networks (ANN) and a hybrid model are used to predict the future selling prices of the fuelwood (from broadleaved and coniferous species) produced by Greek state forest farms. The use of the ARIMA-ANN hybrid model provided the optimum prediction results, thus enabling decision-makers to proceed with a more rational planning for the production and fuelwood market.

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  • Koutroumanidis, Theodoros & Ioannou, Konstantinos & Arabatzis, Garyfallos, 2009. "Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model," Energy Policy, Elsevier, vol. 37(9), pages 3627-3634, September.
  • Handle: RePEc:eee:enepol:v:37:y:2009:i:9:p:3627-3634

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    References listed on IDEAS

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    Cited by:

    1. Zafeiriou, Eleni & Arabatzis, Garyfallos & Koutroumanidis, Theodoros, 2011. "The fuelwood market in Greece: An empirical approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3008-3018, August.
    2. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    3. Azam, Muhammad & Khan, Abdul Qayyum & Bakhtyar, B. & Emirullah, Chandra, 2015. "The causal relationship between energy consumption and economic growth in the ASEAN-5 countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 732-745.
    4. Arabatzis, Garyfallos & Malesios, Chrisovalantis, 2013. "Pro-environmental attitudes of users and non-users of fuelwood in a rural area of Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 621-630.
    5. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    6. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
    7. Azam, Muhammad & Khan, Abdul Qayyum & Zafeiriou, Eleni & Arabatzis, Garyfallos, 2016. "Socio-economic determinants of energy consumption: An empirical survey for Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1556-1567.
    8. Azam, Muhammad & Khan, Abdul Qayyum & Zaman, Khalid & Ahmad, Mehboob, 2015. "Factors determining energy consumption: Evidence from Indonesia, Malaysia and Thailand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1123-1131.
    9. repec:eee:rensus:v:77:y:2017:i:c:p:297-317 is not listed on IDEAS
    10. Arabatzis, Garyfallos & Petridis, Konstantinos & Galatsidas, Spyros & Ioannou, Konstantinos, 2013. "A demand scenario based fuelwood supply chain: A conceptual model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 687-697.
    11. Arabatzis, Garyfallos & Kitikidou, Kyriaki & Tampakis, Stilianos & Soutsas, Konstantinos, 2012. "The fuelwood consumption in a rural area of Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6489-6496.

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