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Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range

Scheepens, Daan R ; Schicker, Irene ; Hlavácková-Schindler, KateÅina ; Plant, Claudia

Geoscientific Model Development, 2023-01, Vol.16 (1), p.251-270 [Peer Reviewed Journal]

Katlenburg-Lindau: Copernicus GmbH

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Title:
Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range
Author: Scheepens, Daan R ; Schicker, Irene ; Hlavácková-Schindler, KateÅina ; Plant, Claudia
Subjects: Adaptation ; Alternative energy sources ; Analysis ; Bias ; Damage prevention ; Deep learning ; Electricity distribution ; Electricity generation ; Energy trading ; Extreme wind speeds ; False alarms ; Forecasting ; Fossil fuels ; Lead time ; Long short-term memory ; Machine learning ; Microbalances ; Multilayers ; Neural networks ; Offshore ; Operators (mathematics) ; Precipitation ; Predictions ; Recurrent neural networks ; Regression models ; Renewable energy ; Root-mean-square errors ; Task scheduling ; Weather forecasting ; Weighting methods ; Wind farms ; Wind power ; Wind power generation ; Wind speed
Is Part Of: Geoscientific Model Development, 2023-01, Vol.16 (1), p.251-270
Description: The number of wind farms and amount of wind power production in Europe, both on- and offshore, have increased rapidly in the past years. To ensure grid stability and on-time (re)scheduling of maintenance tasks and to mitigate fees in energy trading, accurate predictions of wind speed and wind power are needed. Particularly, accurate predictions of extreme wind speed events are of high importance to wind farm operators as timely knowledge of these can both prevent damages and offer economic preparedness. This work explores the possibility of adapting a deep convolutional recurrent neural network (RNN)-based regression model to the spatio-temporal prediction of extreme wind speed events in the short to medium range (12 h lead time in 1 h intervals) through the manipulation of the loss function. To this end, a multi-layered convolutional long short-term memory (ConvLSTM) network is adapted with a variety of imbalanced regression loss functions that have been proposed in the literature: inversely weighted, linearly weighted and squared error-relevance area (SERA) loss. Forecast performance is investigated for various intensity thresholds of extreme events, and a comparison is made with the commonly used mean squared error (MSE) and mean absolute error (MAE) loss. The results indicate the inverse weighting method to most effectively shift the forecast distribution towards the extreme tail, thereby increasing the number of forecasted events in the extreme ranges, considerably boosting the hit rate and reducing the root-mean-squared error (RMSE) in those ranges. The results also show, however, that such improvements are invariably accompanied by a pay-off in terms of increased overcasting and false alarm ratio, which increase both with lead time and intensity threshold. The inverse weighting method most effectively balances this trade-off, with the weighted MAE loss scoring slightly better than the weighted MSE loss. It is concluded that the inversely weighted loss provides an effective way to adapt deep learning to the task of imbalanced spatio-temporal regression and its application to the forecasting of extreme wind speed events in the short to medium range.
Publisher: Katlenburg-Lindau: Copernicus GmbH
Language: English

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Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range

Scheepens, Daan R ; Schicker, Irene ; Hlavácková-Schindler, KateÅina ; Plant, Claudia

Katlenburg-Lindau: Copernicus GmbH

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