Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
- Prybutok, Victor R. & Yi, Junsub & Mitchell, David, 2000. "Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations," European Journal of Operational Research, Elsevier, vol. 122(1), pages 31-40, April.
- Jehlickova, Bohumira & Morris, Richard, 2007. "Effectiveness of policy instruments for supporting the use of waste wood as a renewable energy resource in the Czech Republic," Energy Policy, Elsevier, vol. 35(1), pages 577-585, January.
- Pohekar, S.D. & Kumar, Dinesh & Ramachandran, M., 2005. "Dissemination of cooking energy alternatives in India--a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 9(4), pages 379-393, August.
- Sedjo, Roger A., 1997. "The economics of forest-based biomass supply," Energy Policy, Elsevier, vol. 25(6), pages 559-566, May.
- Arabatzis, Garyfallos, 2008. "The individual and social characteristics of poplar investors-cultivators and the factors that affect the size of poplar plantations according to the EU Regulation 2080/92," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 9(2), June.
- Church, Keith B. & Curram, Stephen P., 1996. "Forecasting consumers' expenditure: A comparison between econometric and neural network models," International Journal of Forecasting, Elsevier, vol. 12(2), pages 255-267, June.
- Katinas, Vladislovas & Markevicius, Antanas, 2006. "Promotional policy and perspectives of usage renewable energy in Lithuania," Energy Policy, Elsevier, vol. 34(7), pages 771-780, May.
- Radetzki, Marian, 1997. "The economics of biomass in industrialized countries: an overview," Energy Policy, Elsevier, vol. 25(6), pages 545-554, May.
- Midilli, Adnan & Dincer, Ibrahim & Ay, Murat, 2006. "Green energy strategies for sustainable development," Energy Policy, Elsevier, vol. 34(18), pages 3623-3633, December.
- Malik, Farooq & Nasereddin, Mahdi, 2006. "Forecasting output using oil prices: A cascaded artificial neural network approach," Journal of Economics and Business, Elsevier, vol. 58(2), pages 168-180.
When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:37:y:2009:i:9:p:3627-3634. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.