To mitigate uncertainties in wind resource assessments and to improve the estimation of
energy production of a wind project, this work uses a decision tree machine learning model …

Intensive condition monitoring of wind generation plant through analysis of routinely
collected SCADA data is seen as a viable means of forestalling costly plant failure and …

Solar power is expected to play a substantial role globally, due to it being one of the leading
renewable electricity sources for future use. Even though the use of solar irradiation to …

Polynomial surrogates are used to characterize the energy production and lifetime
equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine …

Abstract Machine learning is frequently applied in the wind energy industry to build statistical
models of wind farm power production using atmospheric data as input. In the field of wind …

Using detailed upwind and nacelle-based measurements from a General Electric (GE) 1.5
sle model with a 77 m rotor diameter, we calculate power curves and annual energy …

The performance of a ducted wind turbine was simulated in this study utilizing an artificial
neural network under various duct operating conditions. Ducted wind turbines have been …

Wind-generating resources are variable and uncertain compared to traditional power
generation resources. The accurate short-term forecasting of power outputs is essential to …

In recent years, increasingly complex machine learning methods have become state-of-the-
art in modelling wind turbine power curves based on operational data. While these methods …

This paper evaluates the performance of different types of Regression Trees (RTs) in a real
problem of very short-term wind speed prediction from measuring data in wind farms. RT is a …