Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting
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 quant...
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doaj-5a4653ebba60445f86d742c524900f932020-11-25T04:05:28ZengMDPI AGEnergies1996-10732020-10-01135488548810.3390/en13205488Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed ForecastingDaniel Vassallo0Raghavendra Krishnamurthy1Thomas Sherman2Harindra J. S. Fernando3Department of Civil & Environmental Engineering and Earth Sciences (CEEES), University of Notre Dame, Notre Dame, IN 46556, USAPacific Northwest National Laboratory, Richland, WA 99354, USACRCL Solutions, LLC, South Bend, IN 46617, USADepartment of Civil & Environmental Engineering and Earth Sciences (CEEES), University of Notre Dame, Notre Dame, IN 46556, USAAlthough 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.https://www.mdpi.com/1996-1073/13/20/5488wind speed forecastingmachine learningrandom forest |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniel Vassallo Raghavendra Krishnamurthy Thomas Sherman Harindra J. S. Fernando |
spellingShingle |
Daniel Vassallo Raghavendra Krishnamurthy Thomas Sherman Harindra J. S. Fernando Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting Energies wind speed forecasting machine learning random forest |
author_facet |
Daniel Vassallo Raghavendra Krishnamurthy Thomas Sherman Harindra J. S. Fernando |
author_sort |
Daniel Vassallo |
title |
Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting |
title_short |
Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting |
title_full |
Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting |
title_fullStr |
Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting |
title_full_unstemmed |
Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting |
title_sort |
analysis of random forest modeling strategies for multi-step wind speed forecasting |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2020-10-01 |
description |
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. |
topic |
wind speed forecasting machine learning random forest |
url |
https://www.mdpi.com/1996-1073/13/20/5488 |
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