Deep belief network based deterministic and probabilistic wind speed forecasting approach

By Huaizhi Wang, Guibin Wang, G. Q. Li, Jianchun Peng, Yitao Liu. 2016

465 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

How researchers see this paper

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

In Electrical and Electronic Engineering

1. El-Sayed M. El-Alfy
2. Amir Ghaderi
3. Faezeh Ghaderi
4. Borhan Molazem Sanandaji
5. S. Mohammed
6. Khaled A. Althelaya

Citations in the corpus

• Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, methodology): “(Wang et al., 2016)) used a mixture of wavelet transform, deep belief network, and spine quantile regression for wind speed forecasting.”
• Evaluation of bidirectional LSTM for short-and long-term stock market prediction (2018, background): “Applications of time series are vast in many fields such as electrical signal analysis, language processing and speech recognition, traffic analysis, weather forecasting, unemployment rate analysis, inflation dynamics analysis, and many others [1]–[3].”
• Deep Learning on 1-D Biosignals: a Taxonomy-based Survey (2018, methodology): “A simple backpropagation is used to fine-tune and slightly adjust the parameters throughout the network [36].”