Data-Driven Meteorological-Feature-Informed Wind Power Prediction with Machine Learning Decision Trees

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dc.contributor.advisorYu, Meilin
dc.contributor.advisorDelgado, Ruben
dc.contributor.authorSasser, Christiana
dc.contributor.departmentMechanical Engineering
dc.contributor.programEngineering, Mechanical
dc.date.accessioned2022-02-09T15:52:56Z
dc.date.available2022-02-09T15:52:56Z
dc.date.issued2020-01-01
dc.description.abstractUncertainties exist in wind resource assessments during wind farm pre-construction that create under-performance biases in turbine output power. While wind speed is the main factor in power output, other factors, such as lapse rate and considering wind variation throughout the rotor layer, also have an effect on the predicted power output. To mitigate these uncertainties and to improve the estimation of annual energy production of a wind project, this work uses a decision tree machine learning model to assess the effectiveness of hub height wind speed, rotor equivalent wind speed, and lapse rate as variables in power prediction. Data from a scanning Doppler lidar and a meteorological tower are used to train regression trees and predict the output of a two-megawatt wind turbine. To further address how wind speed is characterized throughout the rotor layer, the decision tree model is trained for four vertical wind profile classifications which showcase the need for multiple calculations of wind speed at various levels of the rotor layer. This approach uses the atmospheric data to correlate the power outputs to wind profiles and meteorological characteristics to be able to predict power responses according to physical patterns. Four sets of predictors are used to train the decision tree model and test its prediction capability: hub height wind speed, rotor equivalent wind speed, hub height wind speed combined with lapse rate, and rotor equivalent wind speed combined with lapse rate. Results indicate that when compared to traditional power curve methods, the decision tree combining rotor equivalent wind speed and lapse rate improves prediction accuracy by 22% for the given data set, while also proving to be the most effective method in power prediction for all classified vertical wind profile types. It was observed that models that incorporated lapse rate into predictions performed better than those without it, showcasing the importance of considering atmospheric criteria in wind power prediction analyses.
dc.formatapplication:pdf
dc.genretheses
dc.identifierdoi:10.13016/m2ukqo-fisd
dc.identifier.other12355
dc.identifier.urihttp://hdl.handle.net/11603/24217
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mechanical Engineering Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.sourceOriginal File Name: Sasser_umbc_0434M_12355.pdf
dc.subjectdecision trees
dc.subjectmachine learning
dc.subjectrotor equivalent wind speed
dc.subjectvertical wind profiles
dc.subjectwind energy
dc.subjectwind power prediction
dc.titleData-Driven Meteorological-Feature-Informed Wind Power Prediction with Machine Learning Decision Trees
dc.typeText
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