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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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    4. Takahiro Takamatsu & Hideaki Ohtake & Takashi Oozeki, 2022. "Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation," Energies, MDPI, vol. 15(4), pages 1-18, February.
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    6. Huang, Jing & Qin, Rui, 2024. "Elman neural network considering dynamic time delay estimation for short-term forecasting of offshore wind power," Applied Energy, Elsevier, vol. 358(C).
    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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