An improved residual-based convolutional neural network for very short-term wind power forecasting

An extreme learning machine based very short-term wind power forecasting method for complex terrain

(2020) Hakan Acikgoz et al.   Energy Sources Part A-Recovery Utilization and Environmental Effects

An adaptive time-resolution method for ultra-short-term wind power prediction

(2020) Lijuan Li et al.   INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm

(2020) Ling-Ling Li et al.   JOURNAL OF CLEANER PRODUCTION

A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting

(2019) Yan Hao et al.   APPLIED ENERGY

Multi-step wind power forecast based on VMD-LSTM

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

A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

(2019) Ying-Yi Hong et al.   APPLIED ENERGY

A review of deep learning for renewable energy forecasting

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

A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition

(2019) Hao Yin et al.   ENERGY

A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting

(2018) Chaoshun Li et al.   APPLIED ENERGY

Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction

(2018) Yanfei Li et al.   ENERGY CONVERSION AND MANAGEMENT

Novel Multi-Step Short-Term Wind Power Prediction Framework Based on Chaotic Time Series Analysis and Singular Spectrum Analysis

(2018) Nima Safari et al.   IEEE TRANSACTIONS ON POWER SYSTEMS

Short-term wind power prediction in microgrids using a hybrid approach integrating genetic algorithm, particle swarm optimization, and adaptive neuro-fuzzy inference systems

(2018) Dehua Zheng et al.   IEEJ Transactions on Electrical and Electronic Engineering

A novel deep learning approach for short-term wind power forecasting based on infinite feature selection and recurrent neural network

(2018) Haijian Shao et al.   Journal of Renewable and Sustainable Energy

Image Dehazing Using Residual-Based Deep CNN

(2018) Jinjiang Li et al.   IEEE Access

Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine

(2017) Xiaohui Yuan et al.   ENERGY

Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

(2017) Aneela Zameer et al.   ENERGY CONVERSION AND MANAGEMENT

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

(2017) Kai Zhang et al.   IEEE TRANSACTIONS ON IMAGE PROCESSING

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status

(2017) Panagiotis Korfiatis et al.   JOURNAL OF DIGITAL IMAGING

Accelerating deep neural network training with inconsistent stochastic gradient descent

(2017) Linnan Wang et al.   NEURAL NETWORKS

A hybrid wind power forecasting model based on data mining and wavelets analysis

(2016) R. Azimi et al.   ENERGY CONVERSION AND MANAGEMENT

On the linear convergence of the alternating direction method of multipliers

(2016) Mingyi Hong et al.   MATHEMATICAL PROGRAMMING

Variational Mode Decomposition

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

ARMA based approaches for forecasting the tuple of wind speed and direction

(2010) Ergin Erdem et al.   APPLIED ENERGY