Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM

By Hui Liu, Mi Xiwei, Yan-fei Li. 2018

451 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Energy Engineering and Power Technology

View Paper: https://www.semanticscholar.org/paper/fb021e0045851f02f425478c8d37ea069fda313f

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. Yang Yang
2. Yuhong Liu
3. Zhigang Jin

In Electrical and Electronic Engineering

1. Yang Yang
2. Yuhong Liu
3. Zhigang Jin
4. Weiwei Jiang

In Energy Engineering and Power Technology

1. Yang Yang
2. Yuhong Liu
3. Zhigang Jin

Citations in the corpus

• Stock closing price prediction based on sentiment analysis and LSTM (2019, methodology): “Liu [23] used LSTM to predict wind speed.”
• Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, unknown): “GraphCNN [85], [155], [94], [111], [144], [22], [92], [175], [44], [8], [85], [155] RNN(LSTM,GRU) [42], [77], [165], [99], [91], [163], [35], [159], [64], [38], [135], [181], [88], [81], [190], [37], [169], [166], [41], [65], [192] [126], [27], [177], [90], [23], [89], [178], [17], [179], [101], [97”