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In recent years, transfer learning gained particular interest in the field ofvision and natural language processing. In the research field of vision, e.g.,deep neural networks and transfer learning techniques achieve almost perfectclassification scores within minutes. Nonetheless, these techniques are not yetwidely applied in other domains. Therefore, this article identifies criticalchallenges and shows potential solutions for power forecasts in the field ofrenewable energies. It proposes a framework utilizing transfer learningtechniques in wind power forecasts with limited or no historical data. On theone hand, this allows evaluating the applicability of transfer learning in thefield of renewable energy. On the other hand, by developing automaticprocedures, we assure that the proposed methods provide a framework thatapplies to domains in organic computing as well.

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