Journal of Energy Storage, Год журнала: 2024, Номер 103, С. 114167 - 114167
Опубликована: Окт. 25, 2024
Язык: Английский
Journal of Energy Storage, Год журнала: 2024, Номер 103, С. 114167 - 114167
Опубликована: Окт. 25, 2024
Язык: Английский
Results in Engineering, Год журнала: 2024, Номер 24, С. 103075 - 103075
Опубликована: Окт. 9, 2024
Язык: Английский
Процитировано
6Renewable Energy, Год журнала: 2023, Номер 220, С. 119681 - 119681
Опубликована: Ноя. 18, 2023
Язык: Английский
Процитировано
13Frontiers 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
10Electrical Engineering, Год журнала: 2024, Номер unknown
Опубликована: Июль 24, 2024
Язык: Английский
Процитировано
4Electrical Engineering, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 13, 2024
Язык: Английский
Процитировано
4Journal 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
0Energy, Год журнала: 2025, Номер unknown, С. 135707 - 135707
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Journal of Energy Storage, Год журнала: 2025, Номер 122, С. 116651 - 116651
Опубликована: Апрель 22, 2025
Язык: Английский
Процитировано
0Elsevier eBooks, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Utilities Policy, Год журнала: 2023, Номер 82, С. 101579 - 101579
Опубликована: Май 12, 2023
Язык: Английский
Процитировано
6