Power generation efficiency and resources saving of the hydropower industry using the extended data based convolutional neural network DOI
Jiajun Huang,

Peihao Zheng,

Xuan Hu

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122541 - 122541

Published: Jan. 1, 2025

Language: Английский

A novel method based on time series ensemble model for hourly photovoltaic power prediction DOI

Zenan Xiao,

Xiaoqiao Huang, Jun Liu

et al.

Energy, Journal Year: 2023, Volume and Issue: 276, P. 127542 - 127542

Published: April 20, 2023

Language: Английский

Citations

45
A new paradigm based on Wasserstein Generative Adversarial Network and time-series graph for integrated energy system forecasting DOI
Zhirui Tian, Mei Gai

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 326, P. 119484 - 119484

Published: Jan. 13, 2025

Language: Английский

Citations

2
An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy DOI
Dongmei Xu, Zong Li, Wenchuan Wang

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 629, P. 130558 - 130558

Published: Dec. 7, 2023

Language: Английский

Citations

39
A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting DOI
Xiaoqiao Huang, Jun Liu,

Shaozhen Xu

et al.

Energy, Journal Year: 2023, Volume and Issue: 272, P. 127140 - 127140

Published: March 8, 2023

Language: Английский

Citations

31
Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model DOI Open Access
Yuanyuan Yu, Yu Lin,

Xianping Hou

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 233, P. 120880 - 120880

Published: June 22, 2023

Language: Английский

Citations

27
An interpretable horizontal federated deep learning approach to improve short-term solar irradiance forecasting DOI

Zenan Xiao,

Bixuan Gao, Xiaoqiao Huang

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 436, P. 140585 - 140585

Published: Jan. 1, 2024

Language: Английский

Citations

12
A coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network DOI
Dezhi Liu, Xuan Lin,

Hanyang Liu

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110263 - 110263

Published: March 20, 2025

Language: Английский

Citations

1
Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks DOI

He Zhao,

Xiaoqiao Huang,

Zenan Xiao

et al.

Renewable Energy, Journal Year: 2023, Volume and Issue: 220, P. 119706 - 119706

Published: Nov. 25, 2023

Language: Английский

Citations

19
A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model DOI
Adeel Feroz Mirza, Majad Mansoor, Muhammad Usman

et al.

Energy, Journal Year: 2023, Volume and Issue: 283, P. 129189 - 129189

Published: Sept. 25, 2023

Language: Английский

Citations

18
Recent Trends and Issues of Energy Management Systems Using Machine Learning DOI Creative Commons
Seongwoo Lee, Joonho Seon,

Byung-Sun Hwang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(3), P. 624 - 624

Published: Jan. 27, 2024

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed a promising solution. A comprehensive review current literature trends conducted with focus on key areas, such distributed energy resources, information systems, storage trading risk demand-side grid automation, self-healing systems. The application ML in is discussed, highlighting enhancements data analytics, improvements system facilitation efficient distribution optimization flow. Moreover, architectural frameworks, operational constraints, challenging issues ML-based explored focusing its effectiveness, suitability. This paper intended to provide valuable insights into the future EMS.

Language: Английский

Citations

8