Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

By Shouxiang Wang, N. Zhang, Lei Wu, Yamin Wang. 2016

615 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/db37a243daf047b814f9f0e51a6f2be240eace48

How researchers see this paper

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

In Control and Systems Engineering

1. N. Al‐Ansari
2. Andrés Feijóo
3. Zaher Mundher Yaseen
4. Neeraj Dhanraj Bokde
5. Siyu Tao

In Electrical and Electronic Engineering

1. N. Al‐Ansari
2. Zaher Mundher Yaseen

In Management Science and Operations Research

1. Zaher Mundher Yaseen

Citations in the corpus

• Stock closing price prediction based on sentiment analysis and LSTM (2019, neutral): “Therefore, it has been applied quickly and effectively in different engineering fields, such as focal electroencephalogram signals [37], wind speed [38] and automatic bearing fault diagnosis [39].”
• The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling (2020, background): “This phenomena is discussed more detailly in [33,54].”