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Forecasting accuracy of wind power technology diffusion models across countries

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  • Dalla Valle, Alessandra
  • Furlan, Claudia

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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  • Dalla Valle, Alessandra & Furlan, Claudia, 2011. "Forecasting accuracy of wind power technology diffusion models across countries," International Journal of Forecasting, Elsevier, vol. 27(2), pages 592-601.
    • Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:592-601
    DOI: 10.1016/j.ijforecast.2010.05.018
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    Cited by:

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    5. Mei Yang & Hong Fan & Kang Zhao, 2019. "PM 2.5 Prediction with a Novel Multi-Step-Ahead Forecasting Model Based on Dynamic Wind Field Distance," IJERPH, MDPI, vol. 16(22), pages 1-21, November.
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    12. Huh, Sung-Yoon & Lee, Chul-Yong, 2014. "Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships," Energy Policy, Elsevier, vol. 69(C), pages 248-257.
    13. Dalla Valle, Alessandra & Furlan, Claudia, 2014. "Diffusion of nuclear energy in some developing countries," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 143-153.
    14. Duan, Hong-Bo & Zhu, Lei & Fan, Ying, 2014. "A cross-country study on the relationship between diffusion of wind and photovoltaic solar technology," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 156-169.
    15. Panse, Riddhi & Kathuria, Vinish, 2016. "Role of policy in deployment of wind energy: evidence across states of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 422-432.
    16. Lu, Ze-Yu & Li, Wen-Hua & Xie, Bai-Chen & Shang, Li-Feng, 2015. "Study on China’s wind power development path—Based on the target for 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 197-208.
    17. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
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    19. Xu, Jiuping & Li, Li & Zheng, Bobo, 2016. "Wind energy generation technological paradigm diffusion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 436-449.

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