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Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting

Author

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  • Daniel Vassallo

    (Department of Civil & Environmental Engineering and Earth Sciences (CEEES), University of Notre Dame, Notre Dame, IN 46556, USA)

  • Raghavendra Krishnamurthy

    (Pacific Northwest National Laboratory, Richland, WA 99354, USA)

  • Thomas Sherman

    (CRCL Solutions, LLC, South Bend, IN 46617, UAS)

  • Harindra J. S. Fernando

    (Department of Civil & Environmental Engineering and Earth Sciences (CEEES), University of Notre Dame, Notre Dame, IN 46556, USA)

Abstract

Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ∼8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.

Suggested Citation

  • Daniel Vassallo & Raghavendra Krishnamurthy & Thomas Sherman & Harindra J. S. Fernando, 2020. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting," Energies, MDPI, vol. 13(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5488-:d:431761
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    References listed on IDEAS

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    Cited by:

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    3. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.

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