Short-term load forecasting based on CEEMDAN and Transformer

Electric Power Systems Research, Journal Year: 2022, Volume and Issue: 214, P. 108885 - 108885

Published: Oct. 20, 2022

Language: Английский

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Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture

Applied Energy, Journal Year: 2023, Volume and Issue: 349, P. 121607 - 121607

Published: July 27, 2023

Language: Английский

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

Applied Energy, Journal Year: 2023, Volume and Issue: 348, P. 121439 - 121439

Published: July 7, 2023

Language: Английский

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Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 98, P. 104775 - 104775

Published: July 18, 2023

Language: Английский

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

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 94, P. 104541 - 104541

Published: March 17, 2023

Language: Английский

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An improved attention-based deep learning approach for robust cooling load prediction: Public building cases under diverse occupancy schedules

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 96, P. 104679 - 104679

Published: May 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.

Language: Английский

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Short-term load forecasting method based on secondary decomposition and improved hierarchical clustering

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 101993 - 101993

Published: March 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.

Language: Английский

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Enhancing source domain availability through data and feature transfer learning for building power load forecasting

Building Simulation, Journal Year: 2024, Volume and Issue: 17(4), P. 625 - 638

Published: Jan. 13, 2024

Language: Английский

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An integrated federated learning algorithm for short-term load forecasting

Electric Power Systems Research, Journal Year: 2022, Volume and Issue: 214, P. 108830 - 108830

Published: Oct. 10, 2022

Language: Английский

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An improved hybrid model for short term power load prediction

Energy, Journal Year: 2022, Volume and Issue: 268, P. 126561 - 126561

Published: Dec. 29, 2022

Language: Английский

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