Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss
Ice-growth on wind-turbines can lead to a large reduction of energy production. Since ice-growth on the turbines is not part of standard weather prediction data, forecasts of power production can have large errors when ice-growth occurs. We propose a statistical method based on random-forest regress...
Main Authors: | Sebastian Scher, Jennie Molinder |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8825865/ |
Similar Items
-
Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests
by: Jennie Molinder, et al.
Published: (2021-12-01) -
The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece
by: Evangelos Spiliotis, et al.
Published: (2020-04-01) -
Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction
by: Bogdan Bochenek, et al.
Published: (2021-04-01) -
On the prediction of surface southerly winds at Eilat (Israel).
by: Gabison, Raphael
Published: (1972) -
A diagnostic model for initial winds in primitive equations forecasts.
by: Asselin, Richard
Published: (1970)