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

Daniel Vassallo, Raghavendra Krishnamurthy, Thomas Sherman and Harindra J. S. Fernando
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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

Energies, 2020, vol. 13, issue 20, 1-19

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.

Keywords: wind speed forecasting; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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