Cakformer: Transformer Model for Long-Term Heat Load Forecasting Based on Cauto-Correlation and KAN DOI

T.A.N. Quanwei,

Guijun Xue,

X.I.E. Wenju

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 135460 - 135460

Опубликована: Март 1, 2025

Язык: Английский

Short-term load forecasting based on CEEMDAN and Transformer DOI
Ran Peng,

Kun Dong,

Xu Liu

и другие.

Electric Power Systems Research, Год журнала: 2022, Номер 214, С. 108885 - 108885

Опубликована: Окт. 20, 2022

Язык: Английский

Процитировано

81

Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture DOI
Peijun Zheng, Heng Zhou, Jiang Liu

и другие.

Applied Energy, Год журнала: 2023, Номер 349, С. 121607 - 121607

Опубликована: Июль 27, 2023

Язык: Английский

Процитировано

54

Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system DOI

Lucas Barros Scianni Morais,

Giancarlo Áquila, Victor Augusto Durães de Faria

и другие.

Applied Energy, Год журнала: 2023, Номер 348, С. 121439 - 121439

Опубликована: Июль 7, 2023

Язык: Английский

Процитировано

41

Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM DOI
Arezoo Jahani, Kazem Zare, Leyli Mohammad Khanli

и другие.

Sustainable Cities and Society, Год журнала: 2023, Номер 98, С. 104775 - 104775

Опубликована: Июль 18, 2023

Язык: Английский

Процитировано

32

Predicting the ammonia nitrogen of wastewater treatment plant influent via integrated model based on rolling decomposition method and deep learning algorithm DOI

Kefen Yan,

Chaolin Li,

Ruobin Zhao

и другие.

Sustainable Cities and Society, Год журнала: 2023, Номер 94, С. 104541 - 104541

Опубликована: Март 17, 2023

Язык: Английский

Процитировано

30

An improved attention-based deep learning approach for robust cooling load prediction: Public building cases under diverse occupancy schedules DOI Creative Commons
Chujie Lu, Junhua Gu, Weizhuo Lu

и другие.

Sustainable 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

26

Short-term load forecasting method based on secondary decomposition and improved hierarchical clustering DOI Creative Commons
Wenting Zha, Yongqiang Ji, Liang Chen

и другие.

Results 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

14

Enhancing source domain availability through data and feature transfer learning for building power load forecasting DOI
Fanyue Qian, Yingjun Ruan,

Huiming Lu

и другие.

Building Simulation, Год журнала: 2024, Номер 17(4), С. 625 - 638

Опубликована: Янв. 13, 2024

Язык: Английский

Процитировано

10

An integrated federated learning algorithm for short-term load forecasting DOI
Yang Yang, Zijin Wang, Shangrui Zhao

и другие.

Electric Power Systems Research, Год журнала: 2022, Номер 214, С. 108830 - 108830

Опубликована: Окт. 10, 2022

Язык: Английский

Процитировано

36

An improved hybrid model for short term power load prediction DOI

Jinliang Zhang,

Wang Siya,

Zhongfu Tan

и другие.

Energy, Год журнала: 2022, Номер 268, С. 126561 - 126561

Опубликована: Дек. 29, 2022

Язык: Английский

Процитировано

29