Short-term Wind Power Forecasting Using the Hybrid Model of Improved Variational Mode Decomposition and Maximum Mixture Correntropy Long Short-term Memory Neural Network

A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting

(2021) Yingchao Dong et al.   APPLIED ENERGY

A combination forecasting model of wind speed based on decomposition

(2021) Zhongda Tian et al.   Energy Reports

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

(2020) Sarbjit Singh et al.   CHAOS SOLITONS & FRACTALS

Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

(2020) Dinh Thanh Viet et al.   Energies

Robust semi-supervised support vector machines with Laplace kernel-induced correntropy loss functions

(2020) Hongwei Dong et al.   APPLIED INTELLIGENCE

Preliminary Research of Chaotic Characteristics and Prediction of Short-Term Wind Speed Time Series

(2020) Zhongda Tian   INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS

Short-term wind power forecasting approach based on Seq2Seq model using NWP data

(2020) Yu Zhang et al.   ENERGY

Wind speed forecasting based on variational mode decomposition and improved echo state network

(2020) Huanling Hu et al.   RENEWABLE ENERGY

Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p-Power Error Loss

(2019) Ning Li et al.   Energies

Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model

(2019) Jinhua Zhang et al.   APPLIED ENERGY

Multi-step wind power forecast based on VMD-LSTM

(2019) Li Han et al.   IET Renewable Power Generation

A robust classification framework with mixture correntropy

(2019) Yidan Wang et al.   INFORMATION SCIENCES

Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques

(2019) Aytaç Altan et al.   CHAOS SOLITONS & FRACTALS

A review of deep learning for renewable energy forecasting

(2019) Huaizhi Wang et al.   ENERGY CONVERSION AND MANAGEMENT

Wind power forecast based on improved Long Short Term Memory network

(2019) Li Han et al.   ENERGY

Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction

(2019) Jordan Nielson et al.   ENERGY

Deep Learning Method Based on Gated Recurrent Unit and Variational Mode Decomposition for Short-Term Wind Power Interval Prediction

(2019) Ruoheng Wang et al.   IEEE Transactions on Neural Networks and Learning Systems

Mixture correntropy for robust learning

(2018) Badong Chen et al.   PATTERN RECOGNITION

Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network

(2018) Jyotirmayee Naik et al.   RENEWABLE ENERGY

Interval Deep Generative Neural Network for Wind Speed Forecasting

(2018) Mahdi Khodayar et al.   IEEE Transactions on Smart Grid

A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm

(2018) Ya-Lan Hu et al.   ENERGY CONVERSION AND MANAGEMENT

Performance comparison of ANNs model with VMD for short-term wind speed forecasting

(2018) Mohammed Gendeel et al.   IET Renewable Power Generation

Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis

(2018) Ranjeeta Bisoi et al.   APPLIED SOFT COMPUTING

Short term load forecasting based on feature extraction and improved general regression neural network model

(2018) Yi Liang et al.   ENERGY

Wind power prediction based on variational mode decomposition multi-frequency combinations

(2018) Gang ZHANG et al.   Journal of Modern Power Systems and Clean Energy

A Robust Regression Framework with Laplace Kernel-Induced Loss

(2017) Liming Yang et al.   NEURAL COMPUTATION

Variational Mode Decomposition

(2014) Konstantin Dragomiretskiy et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING