Transfer learning for short-term wind speed prediction with deep neural networks

By Qinghua Hu, Rujia Zhang, Yucan Zhou. 2016

428 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ba73c8328d75eff46d35a5c443d961d364743c8d

How researchers see this paper

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

In Aerospace Engineering

1. Zidong Wang

In Electrical and Electronic Engineering

1. Zidong Wang
2. Xiaohui Liu
3. Yurong Liu
4. Mujeeb Ur Rehman
5. Jinling Liang
6. Peng Shi
7. Zhengguang Wu
8. H. Su

Citations in the corpus

• A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting (2019, background): “One similarity of some of the statistical learning methods mentioned above is that they are shallow deep learning models, while considering the wind power data is complicated, these shallow models may not be sufficient to extract corresponding nonlinear characteristics [22].”
• A survey of deep neural network architectures and their applications (2017, methodology): “DNNs can also be used for the wind speed patterns classification and the supervised multispectral land-use classification [70,105].”
• Deep learning for time series classification: a review (2018, background): “Usually, to model the time series, classifiers are preceded by an unsupervised pre-training phase such as stacked denoising auto-encoders (SDAEs) (Bengio et al., 2013; Hu et al., 2016).”