5 citations · 20 across the 10 of their papers we have counts for
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cs.LG2019★ 5 cited
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid
Jens Schreiber
In recent years, transfer learning gained particular interest in the field of vision and natural language processing. In the research field of vision, e.g., deep neural networks an…
cs.LG2019
Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic
Jens Schreiber, Maik Jessulat, Bernhard Sick
For the integration of renewable energy sources, power grid operators need realistic information about the effects of energy production and consumption to assess grid stability. Re…
stat.AP2019★ 2 cited
Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
Jens Schreiber, Artjom Buschin, Bernhard Sick
Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected for…