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

TL;DR

This paper proposes a transfer learning framework for wind power forecasting that addresses data scarcity issues throughout the lifecycle of wind farms, aiming to improve prediction accuracy in renewable energy applications.

Contribution

It introduces a novel transfer learning framework tailored for renewable energy, enabling power forecasts with limited or no historical data, and discusses its potential for broader organic computing domains.

Findings

Framework effectively improves wind power forecasts with limited data

Transfer learning shows promise in renewable energy applications

Automatic procedures ensure broad applicability

Abstract

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 and transfer learning techniques achieve almost perfect classification scores within minutes. Nonetheless, these techniques are not yet widely applied in other domains. Therefore, this article identifies critical challenges and shows potential solutions for power forecasts in the field of renewable energies. It proposes a framework utilizing transfer learning techniques in wind power forecasts with limited or no historical data. On the one hand, this allows evaluating the applicability of transfer learning in the field of renewable energy. On the other hand, by developing automatic procedures, we assure that the proposed methods provide a framework that applies to domains in organic computing as well.