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A two-step machine learning approach to statistical post-processing of weather forecasts for power generation
By the end of 2021, the renewable energy share of the global electricity capacity reached 38. 3% and the new installations are dominated by wind and solar energy, showing global increases of 12. 7% and 18. 5%, respectively.
Calibration of wind speed ensemble forecasts for power generation
Accurate predictions of wind power require accurate hub height wind speed forecasts, where the state of the art method is the probabilistic approach based on ensemble forecasts obtained from multiple runs of numerical weather prediction models.
Efficient Learning of Model Weights via Changing Features During Training
Our main motivation is to update the model in a small content during the training process with replacing less descriptive features to new ones from a large pool.
Machine learning for total cloud cover prediction
We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor.
