Two-stage optimal configuration and control based on virtual hybrid energy storage DOI
Shujuan Li, Qingshan Xu, Kui Hua

и другие.

Journal of Energy Storage, Год журнала: 2024, Номер 103, С. 114167 - 114167

Опубликована: Окт. 25, 2024

Язык: Английский

Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances DOI Creative Commons

V. Indragandhi,

R. Senthil Kumar,

R. Saranya

и другие.

Results in Engineering, Год журнала: 2024, Номер 24, С. 103075 - 103075

Опубликована: Окт. 9, 2024

Язык: Английский

Процитировано

6

Comparison of control strategies for efficient thermal energy storage to decarbonize residential buildings in cold climates: A focus on solar and biomass sources DOI

Peimaneh Shirazi,

Amirmohammad Behzadi, Pouria Ahmadi

и другие.

Renewable Energy, Год журнала: 2023, Номер 220, С. 119681 - 119681

Опубликована: Ноя. 18, 2023

Язык: Английский

Процитировано

13

CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid DOI Creative Commons

Xuhan Li

Frontiers in Energy Research, Год журнала: 2023, Номер 11

Опубликована: Июль 26, 2023

Introduction: Smart grid (SG) technologies have a wide range of applications to improve the reliability, economics, and sustainability power systems. Optimizing large-scale energy storage for smart grids is an important topic in optimization. By predicting historical load electricity price system, reasonable optimization scheme can be proposed. Methods: Based on this, this paper proposes prediction model combining convolutional neural network (CNN) gated recurrent unit (GRU) based attention mechanism explore grid. The CNN extract spatial features, GRU effectively solve gradient explosion problem long-term forecasting. Its structure simpler faster than LSTM models with similar accuracy. After CNN-GRU extracts data, features are finally weighted by module performance further. Then, we also compared different forecasting models. Results Discussion: results show that our has better predictive computational power, making contribution developing schemes grids.

Язык: Английский

Процитировано

10

IntDEM: an intelligent deep optimized energy management system for IoT-enabled smart grid applications DOI

P. Ganesh,

B. Meenakshi Sundaram, Praveen Kumar Balachandran

и другие.

Electrical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Июль 24, 2024

Язык: Английский

Процитировано

4

Review of load frequency control in modern power systems: a state-of-the-art review and future trends DOI

Samuel Sunday Yusuf,

Abdullahi Bala Kunya,

Adamu Saidu Abubakar

и другие.

Electrical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

4

LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting DOI Open Access
Te Li, Min Zheng, Yan Zhou

и другие.

Journal of Organizational and End User Computing, Год журнала: 2025, Номер 37(1), С. 1 - 29

Опубликована: Фев. 21, 2025

Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional demand forecasting methods often struggle with complex data processing low prediction accuracy. To address these issues, this paper introduces novel approach that combines deep learning techniques decision support systems. The model integrates advanced techniques, including LSTM Transformer, PSO algorithm parameter optimization, significantly enhancing predictive performance practical applicability. Results show our achieves substantial improvements across various metrics, 30% reduction in MAE, 20% decrease MAPE, 25% drop RMSE, 35% decline MSE. These results validate model's effectiveness reliability forecasting. This research provides valuable insights applying

Язык: Английский

Процитировано

0

Long-term policy guidance for sustainable energy transition in Nigeria: A deep learning-based peak load forecasting with econo-environmental scenario analysis DOI
Israel A. Bayode, Abdulrahman H. Ba-Alawi, Hai-Tra Nguyen

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 135707 - 135707

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Enhanced electrical and thermal energy storage systems performance in smart building using FLHNN and BWOA approach DOI

B. Venkata Prasanth,

Karthikeyan Gopalsamy

Journal of Energy Storage, Год журнала: 2025, Номер 122, С. 116651 - 116651

Опубликована: Апрель 22, 2025

Язык: Английский

Процитировано

0

Deep learning in energy conversion systems DOI
Mert Akın İnsel,

Mislina Cakar,

Busranur Ozturk

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Solving electric power distribution uncertainty using deep learning and incentive-based demand response DOI

P. Balakumar,

Vinopraba Thirumavalavan,

Geetha Chandrasekaran

и другие.

Utilities Policy, Год журнала: 2023, Номер 82, С. 101579 - 101579

Опубликована: Май 12, 2023

Язык: Английский

Процитировано

6