Cited by Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning

Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S DOI

Xiaoying Sun, Haizhong Liu

Energy, Journal Year: 2024, Volume and Issue: 305, P. 132228 - 132228

Published: Oct. 1, 2024

Language: Английский

Citations

The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer DOI

Guihua Ban,

Yan Chen, Zhenhua Xiong

et al.

Energy, Journal Year: 2024, Volume and Issue: 290, P. 130225 - 130225

Published: Jan. 1, 2024

Language: Английский

Citations

Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks DOI

Dan Li, Yijun Li, Chaoqun Wang

et al.

Applied Energy, Journal Year: 2022, Volume and Issue: 331, P. 120452 - 120452

Published: Dec. 8, 2022

Language: Английский

Citations

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine DOI

Emrah Dokur, Nuh Erdoğan, Mahdi Ebrahimi Salari

et al.

Energy, Journal Year: 2022, Volume and Issue: 248, P. 123595 - 123595

Published: March 3, 2022

As the share of global offshore wind energy in electricity generation portfolio is rapidly increasing, grid integration large-scale farms becoming interest. Due to intermittency wind, stability power systems challenging. Therefore, accurate and fast short-term speed forecasting tools play important role maintaining reliability safe operation system. This paper proposes a novel hybrid model based on swarm decomposition (SWD) meta-extreme learning machine (Meta-ELM). approach combines advantages SWD which has proven efficiency for non-stationary signals, with Meta-ELM provides faster calculation lower computational burden. In order enhance accuracy stability, signal decomposed by implementing swarm-prey hunting algorithm SWD. To validate model, comparison against four conventional state-of-the-art models performed. The implemented are tested two real datasets. results demonstrate that proposed outperforms counterparts all performance metrics considered. can also improve as well-known robust method.

Language: Английский

Citations

Review of load forecasting based on artificial intelligence methodologies, models, and challenges DOI

Hui Hou, Chao Liu, Qing Wang

et al.

Electric Power Systems Research, Journal Year: 2022, Volume and Issue: 210, P. 108067 - 108067

Published: May 11, 2022

Language: Английский

Citations

Wind power forecasting based on new hybrid model with TCN residual modification DOI

Jiaojiao Zhu,

Liancheng Su, Yingwei Li

et al.

Energy and AI, Journal Year: 2022, Volume and Issue: 10, P. 100199 - 100199

Published: Sept. 6, 2022

Wind energy has been widely utilized to alleviate the shortage of fossil resources. When wind power is integrated into grid on a large scale, grid's stability severely harmed due fluctuating and intermittent properties speed. Accurate forecasts help formulate good operational strategies for farms. A short-term forecasting method based new hybrid model proposed increase accuracy forecast. Firstly, time series are separated using complete ensemble empirical mode decomposition with adaptive noise obtain multiple components, which then predicted support vector regression machine optimized through search cross validation (GridSearchCV) algorithm. Secondly, residual modification temporal convolutional network constructed, variables high correlation selected as input features predict residuals power. Finally, prediction compared other models actual data farm demonstrate validity described method, results reveal that better performance. © 2017 Elsevier Inc. All rights reserved.

Language: Английский

Citations

A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting DOI

Jianzhou Wang, Yining An, Zhiwu Li

et al.

Energy, Journal Year: 2022, Volume and Issue: 251, P. 123960 - 123960

Published: April 11, 2022

Language: Английский

Citations

An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting DOI

Xiaoran Dai, Shuai Liu, Wenshan Hu

et al.

Energy, Journal Year: 2023, Volume and Issue: 272, P. 127173 - 127173

Published: March 10, 2023

Language: Английский

Citations

Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field DOI

Zhen Shao,

Jun Han, Wei Zhao

et al.

Energy Conversion and Management, Journal Year: 2022, Volume and Issue: 269, P. 116138 - 116138

Published: Sept. 1, 2022

Language: Английский

Citations

Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN DOI

Chenjia Hu,

Yan Zhao, He Jiang

et al.

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 483 - 492

Published: Oct. 13, 2022

So as to decrease those cacoethic impact of a huge amount wind energy generation systems associated with the electric power system and improve utilization rate budgetary profits era, this paper raises neural network in view CEEMDAN-LSTM-TCN. Firstly, CEEMDAN is used break down velocity arrangement sway arbitrariness Furthermore variance about velocity. Secondly, ultra-short-term forecast depend upon LSTM TCN built realize real-time prediction for energy. Finally, simulation results show that LSTM-TCN can deal multi time order characteristics predict ultra-short period effect, which better than TCN. It also has scientific reference local dispatching.

Language: Английский

Citations