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Found 5 papers, 2 papers with code
Targeted Adversarial Attacks on Wind Power Forecasts
In recent years, researchers proposed a variety of deep learning models for wind power forecasting.
Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning
Retraining deep neural networks when new data arrives is typically computationally expensive.
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast
The proposed architecture significantly improves up to 25 percent for multi-task learning for power forecasts on the EuropeWindFarm and GermanSolarFarm dataset compared to the multi-layer perceptron approach.
Synthetic Photovoltaic and Wind Power Forecasting Data
Since the synthetic time series are based exclusively on weather measurements, possible errors in the weather forecast are comparable to those in actual power data.
Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets
In this article, we present a novel approach to multivariate probabilistic forecasting.
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