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

Load Forecasting Techniques for Power System: Research Challenges and Survey DOI

Naqash Ahmad,

Yazeed Yasin Ghadi,

Muhammad Adnan

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 71054 - 71090

Published: Jan. 1, 2022

The main and pivot part of electric companies is the load forecasting. Decision-makers think tank power sectors should forecast future need electricity with large accuracy small error to give uninterrupted free shedding consumers. demand can be forecasted amicably by many Machine Learning (ML), Deep (DL) Artificial Intelligence (AI) techniques among which hybrid methods are most popular. present technologies forecasting work regarding combination various ML, DL AI algorithms reviewed in this paper. comprehensive review single models functions; advantages disadvantages discussed comparison between performance terms Mean Absolute Error (MAE), Root Squared (RMSE), Percentage (MAPE) values compared literature different support researchers select best model for prediction. This validates fact that will provide a more optimal solution.

Language: Английский

Citations

138

Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting DOI

Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2021, Volume and Issue: 136, P. 107712 - 107712

Published: Oct. 29, 2021

Language: Английский

Citations

123

Ultra-short term wind power prediction applying a novel model named SATCN-LSTM DOI

Ling Xiang, Jianing Liu, Xin Yang

et al.

Energy Conversion and Management, Journal Year: 2021, Volume and Issue: 252, P. 115036 - 115036

Published: Dec. 2, 2021

Language: Английский

Citations

110

An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction DOI

Chu Zhang,

Huixin Ma,

Lei Hua

et al.

Energy, Journal Year: 2022, Volume and Issue: 254, P. 124250 - 124250

Published: May 14, 2022

Language: Английский

Citations

Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization DOI

Sheng-Xiang Lv,

Lin Wang

Applied Energy, Journal Year: 2022, Volume and Issue: 311, P. 118674 - 118674

Published: Feb. 12, 2022

Language: Английский

Citations

A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error DOI

Jikai Duan, Mingheng Chang,

Xiangyue Chen

et al.

Renewable Energy, Journal Year: 2022, Volume and Issue: 200, P. 788 - 808

Published: Oct. 4, 2022

Language: Английский

Citations

Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition DOI

Changrui Deng,

Yanmei Huang, Najmul Hasan

et al.

Information Sciences, Journal Year: 2022, Volume and Issue: 607, P. 297 - 321

Published: June 4, 2022

Language: Английский

Citations

Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction DOI

Chu Zhang, Chunlei Ji, Lei Hua

et al.

Renewable Energy, Journal Year: 2022, Volume and Issue: 197, P. 668 - 682

Published: Aug. 2, 2022

Language: Английский

Citations

Deep Learning for Time Series Forecasting: Advances and Open Problems DOI

Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo

et al.

Information, Journal Year: 2023, Volume and Issue: 14(11), P. 598 - 598

Published: Nov. 4, 2023

A time series is a sequence of time-ordered data, and it generally used to describe how phenomenon evolves over time. Time forecasting, estimating future values series, allows the implementation decision-making strategies. Deep learning, currently leading field machine applied forecasting can cope with complex high-dimensional that cannot be usually handled by other learning techniques. The aim work provide review state-of-the-art deep architectures for underline recent advances open problems, also pay attention benchmark data sets. Moreover, presents clear distinction between are suitable short-term long-term forecasting. With respect existing literature, major advantage consists in describing most such as Graph Neural Networks, Gaussian Processes, Generative Adversarial Diffusion Models, Transformers.

Language: Английский

Citations

Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN DOI

Qingyang Li, Guosong Wang, Xinrong Wu

et al.

Energy, Journal Year: 2024, Volume and Issue: 299, P. 131448 - 131448

Published: April 27, 2024

Language: Английский

Citations