Physics-Guided Machine Learning for Wind-Farm Power Prediction: Toward Interpretability and Generalizability
Navid Zehtabiyan-Rezaie, Alexandros Iosifidis, Mahdi Abkar
Abstract
A machine-learning model is developed and used to predict the performance of individual wind turbines in wind farms; the strategy leads to an accurate, lightweight, and generalizable data-driven model for wind-farm power prediction.
Topics & Concepts
InterpretabilityGeneralizability theoryWind powerMachine learningPower (physics)Predictive powerComputer scienceOffshore wind powerArtificial intelligenceEngineeringElectrical engineeringPhysicsMathematicsStatisticsQuantum mechanicsWind Energy Research and DevelopmentEnergy Load and Power ForecastingSolar Radiation and Photovoltaics