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Introducing the Temporal Distortion Index to perform a bidimensional analysis of renewable energy forecast

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  • Frías-Paredes, Laura
  • Mallor, Fermín
  • León, Teresa
  • Gastón-Romeo, Martín

Abstract

Wind has been the largest contributor to the growth of renewal energy during the early 21st century. However, the natural uncertainty that arises in assessing the wind resource implies the occurrence of wind power forecasting errors which perform a considerable role in the impacts and costs in the wind energy integration and its commercialization. The main goal of this paper is to provide a deeper insight in the analysis of timing errors which leads to the proposal of a new methodology for its control and measure. A new methodology, based on Dynamic Time Warping, is proposed to be considered in the estimation of accuracy as attribute of forecast quality. A new dissimilarity measure, the Temporal Distortion Index, among time series is introduced to complement the traditional verification measures found in the literature. Furthermore we provide a bi-criteria perspective to the problem of comparing different forecasts. The methodology is illustrated with several examples including a real case.

Suggested Citation

  • Frías-Paredes, Laura & Mallor, Fermín & León, Teresa & Gastón-Romeo, Martín, 2016. "Introducing the Temporal Distortion Index to perform a bidimensional analysis of renewable energy forecast," Energy, Elsevier, vol. 94(C), pages 180-194.
    • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:180-194
    DOI: 10.1016/j.energy.2015.10.093
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    1. Kaldellis, John K. & Zafirakis, D., 2011. "The wind energy (r)evolution: A short review of a long history," Renewable Energy, Elsevier, vol. 36(7), pages 1887-1901.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    4. Azcárate, Cristina & Blanco, Rosa & Mallor, Fermín & Garde, Raquel & Aguado, Mónica, 2012. "Peaking strategies for the management of wind-H2 energy systems," Renewable Energy, Elsevier, vol. 47(C), pages 103-111.
    5. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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    7. Ahn, Hyeunguk, 2024. "A framework for developing data-driven correction factors for solar PV systems," Energy, Elsevier, vol. 290(C).

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