Energy, Год журнала: 2025, Номер unknown, С. 135460 - 135460
Опубликована: Март 1, 2025
Язык: Английский
Energy, Год журнала: 2025, Номер unknown, С. 135460 - 135460
Опубликована: Март 1, 2025
Язык: Английский
Electric Power Systems Research, Год журнала: 2022, Номер 214, С. 108885 - 108885
Опубликована: Окт. 20, 2022
Язык: Английский
Процитировано
81Applied Energy, Год журнала: 2023, Номер 349, С. 121607 - 121607
Опубликована: Июль 27, 2023
Язык: Английский
Процитировано
54Applied Energy, Год журнала: 2023, Номер 348, С. 121439 - 121439
Опубликована: Июль 7, 2023
Язык: Английский
Процитировано
41Sustainable Cities and Society, Год журнала: 2023, Номер 98, С. 104775 - 104775
Опубликована: Июль 18, 2023
Язык: Английский
Процитировано
32Sustainable Cities and Society, Год журнала: 2023, Номер 94, С. 104541 - 104541
Опубликована: Март 17, 2023
Язык: Английский
Процитировано
30Sustainable Cities and Society, Год журнала: 2023, Номер 96, С. 104679 - 104679
Опубликована: Май 28, 2023
Space cooling in buildings is responsible for massive energy consumption and carbon emissions. Accurate load prediction can facilitate the implementation of energy-efficiency control strategies practice. In this paper, an improved attention-based deep learning approach proposed robust ultra-short-term prediction. First, a novel time representation introduced to extract periodicity non-periodicity loads efficiently. Then, long short-term memory with attention mechanism extracts properly steps by identifying relevant hidden states learns high-level temporal dependency. The additionally incorporates extreme gradient boosting through error reciprocal method, enhancing elimination errors improving robustness. study takes Guangzhou as example generates using diverse occupancy schedules five building types based on Chinese National Standard Typical Meteorological Year data. evaluated datasets comprising loads, meteorological data, contextual information. Through results analysis, outperforms other models terms accuracy robustness across all types. Additionally, model interpretation provided regarding feature importance matrixes, which enhances understanding transparency final from approach.
Язык: Английский
Процитировано
26Results in Engineering, Год журнала: 2024, Номер 22, С. 101993 - 101993
Опубликована: Март 15, 2024
In the context of large-scale grid connection new energy, short-term load forecasting is a vital and challenging task for power system to balance supply demand. To effectively improve accuracy, method proposed aiming mine characteristics data study application artificial intelligence algorithms. this paper, seasonal trend decomposition using loess (STL) firstly applied decompose into trend, residual components component with highest complexity further decomposed by complete ensemble empirical mode adaptive noise (CEEMDAN) approach. Secondly, in order reduce number components, improved hierarchical clustering technique cluster all intrinsic functions (IMFs) obtained CEEMDAN high-frequency low-frequency components. Then, different network models are trained get prediction results total value achieved stacking them. Finally, national demand dataset Great Britain 2021–2022 used conduct ablation comparative experiments. The mean absolute percentage error (MAPE) root square (RMSE) 2.064% 724.01 MW, respectively, which verified effectiveness advancement method.
Язык: Английский
Процитировано
14Building Simulation, Год журнала: 2024, Номер 17(4), С. 625 - 638
Опубликована: Янв. 13, 2024
Язык: Английский
Процитировано
10Electric Power Systems Research, Год журнала: 2022, Номер 214, С. 108830 - 108830
Опубликована: Окт. 10, 2022
Язык: Английский
Процитировано
36Energy, Год журнала: 2022, Номер 268, С. 126561 - 126561
Опубликована: Дек. 29, 2022
Язык: Английский
Процитировано
29