A short-term wind power prediction approach based on ensemble empirical mode decomposition and improved long short-term memory

Tianyue Jiang; Yutong Liu

doi:10.1016/j.compeleceng.2023.108830

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A short-term wind power prediction approach based on ensemble empirical mode decomposition and improved long short-term memory

Tianyue Jiang Yutong Liu

Year: 2023 Journal: Computers & Electrical Engineering Vol: 110 Pages: 108830-108830 Publisher: Elsevier BV

DOI: 10.1016/j.compeleceng.2023.108830

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Keywords:

Hilbert–Huang transform Computer science Wind power Particle swarm optimization Hyperparameter Term (time) Mode (computer interface) Series (stratigraphy) Volatility (finance) Long short term memory Algorithm Artificial intelligence Energy (signal processing) Mathematics Artificial neural network Engineering Econometrics Recurrent neural network Statistics

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Energy Load and Power Forecasting

Physical Sciences → Engineering → Electrical and Electronic Engineering

Machine Fault Diagnosis Techniques

Physical Sciences → Engineering → Control and Systems Engineering

Power Systems and Renewable Energy

Physical Sciences → Energy → Energy Engineering and Power Technology

A short-term wind power prediction approach based on ensemble empirical mode decomposition and improved long short-term memory

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