Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains

TL;DR

This paper presents an adaptive transfer learning approach for deep neural networks to improve short-term wind power prediction by transferring knowledge across regions and different wind-related tasks, enhancing learning efficiency and accuracy.

Contribution

It introduces ATL-DNN, a novel adaptive transfer learning method that dynamically adjusts to new wind farm data and transfers knowledge between different wind power and wind speed prediction tasks.

Findings

Achieves low error metrics in wind power prediction

Effectively transfers knowledge across regions and tasks

Enhances learning with incoming data

Abstract

Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the applications, the labeling of data is costly and time-consuming. Additionally, TL also provides an effective weight initialization strategy for Deep Neural Networks . This paper introduces the idea of Adaptive Transfer Learning in Deep Neural Networks (ATL-DNN) for wind power prediction. Specifically, we show in case of wind power prediction that adaptive TL of Deep Neural Networks system can be adaptively modified as regards training on a different wind farm is concerned. The proposed ATL-DNN technique is tested for short-term wind power prediction, where continuously arriving information has to be exploited. Adaptive TL not only helps in providing good weight initialization, but is also helpful to utilize the incoming data for effective learning. Additionally, the proposed ATL-DNN technique is shown to transfer knowledge between different task domains (wind power to wind speed prediction) and from one region to another region. The simulation results show that the proposed ATL-DNN technique achieves average values of 0.0637,0.0986, and 0.0984 for the Mean-Absolute-Error, Root-Mean-Squared-Error, and Standard-Deviation-Error, respectively.