Wind power prediction using deep neural network based meta regression and transfer learning

By Aqsa Saeed Qureshi, Asifullah Khan, Aneela Zameer, A. Usman. Applied Soft Computing, 2017

341 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/beeb4a0429adc9c900ad758ccb5d30dd64d8da74

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shibin Wang
2. Xiaolei Yu
3. Qiyang Zhang
4. Chuang Sun
5. Ruqiang Yan
6. Xuefeng Chen
7. Zhibin Zhao

In Electrical and Electronic Engineering

1. Ruqiang Yan
2. Xuefeng Chen

Citations in the corpus

• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2019, positive): “Qureshi et al. [48] pre-trained nine deep sparse auto-encoders on one wind farm, and predictions on another wind farm were taken by fine-tuning the pre-trained networks.”