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Forecasting accuracy of wind power technology diffusion models across countries

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  • Dalla Valle, Alessandra
  • Furlan, Claudia
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    Abstract

    Wind power technology is analyzed in terms of diffusion, with incentive effects introduced as exogenous dynamics in the Generalized Bass Model (GBM) framework. Estimates and short-term forecasts of the life-cycles of wind power are provided for the US and Europe, as they have similar geographic areas, as well as for some leading European countries. GBMs have the best performance in model selection, and are ranked first in terms of forecast accuracy over a set of different accuracy measures and forecasting horizons, relative to the Standard Bass, Logistic, and Gompertz models.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 2 (April)
    Pages: 592-601

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    Handle: RePEc:eee:intfor:v:27:y::i:2:p:592-601

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Diffusion models Bass Logistic Gompertz Wind power Forecasting accuracy Incentives;

    References

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    1. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    2. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    3. Islam, Towhidul & Fiebig, Denzil G. & Meade, Nigel, 2002. "Modelling multinational telecommunications demand with limited data," International Journal of Forecasting, Elsevier, vol. 18(4), pages 605-624.
    4. Ben Maalla, El Mehdi & Kunsch, Pierre L., 2008. "Simulation of micro-CHP diffusion by means of System Dynamics," Energy Policy, Elsevier, vol. 36(7), pages 2308-2319, July.
    5. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    6. Snyder, Brian & Kaiser, Mark J., 2009. "A comparison of offshore wind power development in europe and the U.S.: Patterns and drivers of development," Applied Energy, Elsevier, vol. 86(10), pages 1845-1856, October.
    7. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    8. Patrik Söderholm & Ger Klaassen, 2007. "Wind Power in Europe: A Simultaneous Innovation–Diffusion Model," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 36(2), pages 163-190, February.
    9. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
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