Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network
Ultra-short-term charging load prediction is of crucial importance for the real-time scheduling and stable operation of power systems. To address the problem of the strong nonlinearity of power load sequences, this study proposes an ultra-short-term charging load prediction model that combines a Tim...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10945330/ |
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| author | Shaoyang Yin Zhaohui Chen Wanyuan Liu Zhiwen Su |
| author_facet | Shaoyang Yin Zhaohui Chen Wanyuan Liu Zhiwen Su |
| author_sort | Shaoyang Yin |
| collection | DOAJ |
| description | Ultra-short-term charging load prediction is of crucial importance for the real-time scheduling and stable operation of power systems. To address the problem of the strong nonlinearity of power load sequences, this study proposes an ultra-short-term charging load prediction model that combines a Time Convolutional Network-Bidirectional Gated Recurrent Unit (TCN-Bi-GRU)and uses the Osprey Optimization Algorithm (OOA) based on the opposition-based learning strategy to optimize Variational Mode Decomposition (VMD). First, an OOA incorporating an opposition-based learning strategy was introduced to optimize the key parameters of the VMD. The permutation entropy is utilized as the fitness function to iteratively obtain the optimal combination of fitness parameters.Secondly, VMD with the optimal parameter combination is used to decompose the original charging load sequence into multiple relatively stable components, reducing the non-stationarity and complexity of the sequence. Then, the components and external data are divided into training sets to train TCN-Bi-GRU. After the model training was completed, the test sets of each component were predicted separately to fully explore the spatiotemporal characteristics of the dataset. The experimental results show that compared with single models, the proposed model performs better in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared in three different scenarios, proving that the model has high prediction accuracy and good robustness in ultra-short-term charging load prediction. |
| format | Article |
| id | doaj-art-3fc41f8f00e64da6848c33c57585482d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3fc41f8f00e64da6848c33c57585482d2025-08-20T02:16:40ZengIEEEIEEE Access2169-35362025-01-0113587785878910.1109/ACCESS.2025.355573710945330Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural NetworkShaoyang Yin0Zhaohui Chen1Wanyuan Liu2Zhiwen Su3https://orcid.org/0009-0009-2078-4061Yunnan Power Grid Company Ltd., Kunming, ChinaYunnan Power Grid Company Ltd., Kunming, ChinaYunnan Power Grid Company Ltd., Kunming, ChinaYantai Dongfang Wisdom Electric Company Ltd., Yantai, ChinaUltra-short-term charging load prediction is of crucial importance for the real-time scheduling and stable operation of power systems. To address the problem of the strong nonlinearity of power load sequences, this study proposes an ultra-short-term charging load prediction model that combines a Time Convolutional Network-Bidirectional Gated Recurrent Unit (TCN-Bi-GRU)and uses the Osprey Optimization Algorithm (OOA) based on the opposition-based learning strategy to optimize Variational Mode Decomposition (VMD). First, an OOA incorporating an opposition-based learning strategy was introduced to optimize the key parameters of the VMD. The permutation entropy is utilized as the fitness function to iteratively obtain the optimal combination of fitness parameters.Secondly, VMD with the optimal parameter combination is used to decompose the original charging load sequence into multiple relatively stable components, reducing the non-stationarity and complexity of the sequence. Then, the components and external data are divided into training sets to train TCN-Bi-GRU. After the model training was completed, the test sets of each component were predicted separately to fully explore the spatiotemporal characteristics of the dataset. The experimental results show that compared with single models, the proposed model performs better in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared in three different scenarios, proving that the model has high prediction accuracy and good robustness in ultra-short-term charging load prediction.https://ieeexplore.ieee.org/document/10945330/Ultra-short-term load predictionvariational mode decomposition (VMD)time convolutional neural network (TCN)osprey optimization algorithm (OOA) and combined model |
| spellingShingle | Shaoyang Yin Zhaohui Chen Wanyuan Liu Zhiwen Su Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network IEEE Access Ultra-short-term load prediction variational mode decomposition (VMD) time convolutional neural network (TCN) osprey optimization algorithm (OOA) and combined model |
| title | Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network |
| title_full | Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network |
| title_fullStr | Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network |
| title_full_unstemmed | Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network |
| title_short | Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network |
| title_sort | ultra short term charging load forecasting based on improved data decomposition and hybrid neural network |
| topic | Ultra-short-term load prediction variational mode decomposition (VMD) time convolutional neural network (TCN) osprey optimization algorithm (OOA) and combined model |
| url | https://ieeexplore.ieee.org/document/10945330/ |
| work_keys_str_mv | AT shaoyangyin ultrashorttermchargingloadforecastingbasedonimproveddatadecompositionandhybridneuralnetwork AT zhaohuichen ultrashorttermchargingloadforecastingbasedonimproveddatadecompositionandhybridneuralnetwork AT wanyuanliu ultrashorttermchargingloadforecastingbasedonimproveddatadecompositionandhybridneuralnetwork AT zhiwensu ultrashorttermchargingloadforecastingbasedonimproveddatadecompositionandhybridneuralnetwork |