Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting

By Mahdi Khodayar, Okyay Kaynak, Mohammad E. Khodayar. IEEE Transactions on Industrial Informatics, 2017

306 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/322c70e0db11f1f0ca7cfe389f7e64ff1c7d0374

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Jianhui Wang
2. Tao Huang
3. Ettore Francesco Bompard
4. Yang Zhang
5. Mahdi Khodayar

In Electrical and Electronic Engineering

1. Jianhui Wang
2. Tao Huang
3. Ettore Francesco Bompard
4. Yang Zhang
5. Mahdi Khodayar

Citations in the corpus

• Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting (2019, methodology): “, Deep Belief Network (DBN) [24] and Stacked Auto-encoder (SAE) [25], [26].”
• Big data analytics in smart grids: a review (2018, background): “Reference (Khodayar et al., 2017) studies the ultra-short-term wind forecasting with the deep learning method through unsupervised feature learning from the unlabeled historical wind speed data.”
• Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications (2020, background): “In the construction of the interval deep generative artifi-cial neural networks [60] and the rough auto encoders [61], a rough set theory is introduced to the deep learning algo-rithm to deal with data ambiguity.”