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The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting

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  • Stefano Alessandrini

    (Research Applications Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80307-3000, USA)

  • Tyler McCandless

    (Research Applications Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80307-3000, USA)

Abstract

One way to mitigate the variability of wind and solar power generation is to install the corresponding plants in nearby locations. For example, in Kuwait, the facility at Shagaya Renewable Energy Park is located in a desert area with both photovoltaic panels and wind turbines that allow the continuous generation of renewable energy throughout the day. The National Center for Atmospheric Research (NCAR) has developed a system to generate probabilistic wind and solar predictions for the Shagaya facility. These predictions are based on the analog ensemble technique that post-processes the wind speed and solar irradiance predictions based on a combination of multiple models including the Weather Research and Forecasting (WRF) numerical model. The ensemble forecasts have 20 members and are generated independently at each wind and solar power production facility. Here we present a method based on the Schaake Shuffle (SS) technique to pair the ensemble members from the independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation. After reordering through the SS technique, the corresponding paired solar and wind power members can be summed to build a unique ensemble of combined generation that is statistically consistent, as verified by the presented metrics.

Suggested Citation

  • Stefano Alessandrini & Tyler McCandless, 2020. "The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting," Energies, MDPI, vol. 13(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2503-:d:358844
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    References listed on IDEAS

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    Cited by:

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    2. Sue Ellen Haupt & Tyler C. McCandless & Susan Dettling & Stefano Alessandrini & Jared A. Lee & Seth Linden & William Petzke & Thomas Brummet & Nhi Nguyen & Branko Kosović & Gerry Wiener & Tahani Hussa, 2020. "Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting," Energies, MDPI, vol. 13(8), pages 1-23, April.
    3. Georgios E. Arnaoutakis & Georgia Kefala & Eirini Dakanali & Dimitris Al. Katsaprakakis, 2022. "Combined Operation of Wind-Pumped Hydro Storage Plant with a Concentrating Solar Power Plant for Insular Systems: A Case Study for the Island of Rhodes," Energies, MDPI, vol. 15(18), pages 1-23, September.
    4. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).

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