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Refining the Selection of Historical Period in Analog Ensemble Technique

Author

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  • Federico E. del Pozo

    (Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    Energy Engineering, University of Science and Technology, Daejeon 34113, Republic of Korea
    Department of Science and Technology, Industrial Technology Development Institute, Taguig 1631, Philippines)

  • Chang Ki Kim

    (Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    Energy Engineering, University of Science and Technology, Daejeon 34113, Republic of Korea)

  • Hyun-Goo Kim

    (Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    Energy Engineering, University of Science and Technology, Daejeon 34113, Republic of Korea)

Abstract

A precise estimate of solar energy output is essential for its efficient integration into the power grid as solar energy becomes a more significant renewable energy source. Contrarily, the creation of solar energy involves fluctuation and uncertainty. The integration and operation of energy systems are complicated by the uncertainty in solar energy projection. As a post-processing technique to lower systematic and random errors in the operational meteorological forecast model, the analog ensemble algorithm will be introduced in this study. When determining the appropriate historical and predictive data required to use the approach, an optimization is conducted for the historical period in order to further maximize the capabilities of the analog ensemble. To determine statistical consistency and spread skill, the model is evaluated against both the raw forecast model and observations. The outcome lowers the uncertainty in the predicted data by demonstrating that statistical findings improve significantly even with 1-month historical data. Nevertheless, the optimization with a year’s worth of historical data demonstrates a notable decrease in the outcomes, limiting overestimation and lowering uncertainty. Specifically, analog ensemble algorithms calibrate analog forecasts that are equivalent to the latest target forecasts within a set of previous deterministic forecasts. Overall, we conclude that analog ensembles assuming a 1-year historical period offer a comprehensive method to minimizing uncertainty and that they should be carefully assessed given the specific forecasting aims and limits.

Suggested Citation

  • Federico E. del Pozo & Chang Ki Kim & Hyun-Goo Kim, 2023. "Refining the Selection of Historical Period in Analog Ensemble Technique," Energies, MDPI, vol. 16(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7630-:d:1282514
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    References listed on IDEAS

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    1. Vanvyve, Emilie & Delle Monache, Luca & Monaghan, Andrew J. & Pinto, James O., 2015. "Wind resource estimates with an analog ensemble approach," Renewable Energy, Elsevier, vol. 74(C), pages 761-773.
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