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State Prediction Method for Digital Twins of Power Distribution Networks by Integrating Temperature Factors with Wavelet LSTM
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Dong-li JIA1, Tian-yuan KANG1, Shuai WANG1, Yi AN2, Qin-ya QI2, Yong-chao LIAN3
Science Technology and Engineering | 2025, 25(7) : 2817 - 2824
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Science Technology and Engineering | 2025, 25(7): 2817-2824
Papers·Electrical Technology
State Prediction Method for Digital Twins of Power Distribution Networks by Integrating Temperature Factors with Wavelet LSTM
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Dong-li JIA1, Tian-yuan KANG1, Shuai WANG1, Yi AN2, Qin-ya QI2, Yong-chao LIAN3
Affiliations
  • 1 China Electric Power Research Institute, Beijing 100192, China
  • 2 State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
  • 3 Dongfang Electronics Co., Ltd., Yantai 264011, China
Published: 2025-03-08 doi: 10.12404/j.issn.1671-1815.2403421
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The digital twin technology of the distribution network is an important product resulting from the integration and development of the power system and information technology. The technology simulates the physical behavior and operational status of the distribution network in a digital space by constructing a virtual model of the physical distribution network, enabling comprehensive simulation and analysis. Due to the diverse systems and complex states involved, the existing digital twin simulation platform technology for distribution networks still requires improvement. A wavelet-LSTM fusion model for power state and weather factors was constructed based on the existing wavelet transform and long short-term memory (LSTM) neural network. The high-dimensional input data were converted into detail and contour coefficients using discrete wavelet transform. Subsequently, LSTM neural networks were constructed to process the data and fuse the results, thereby forming accurate prediction outcomes. This method was validated on real datasets, showing that the wavelet-LSTM fusion model significantly improves the mean absolute percentage error (MAPE) compared to the existing LSTM network. Additionally, the method was tested on datasets from different industries. Compared to wavelet-Lasso, LSTM, and STL-LSTM, it exhibits better performance in terms of MAPE, demonstrating that the wavelet LSTM prediction method can be applied to state data from various sectors, thereby providing robust support for future state prediction of digital twins.

digital twin  /  smart grid  /  distribution network  /  wavelet-LSTM
Dong-li JIA, Tian-yuan KANG, Shuai WANG, Yi AN, Qin-ya QI, Yong-chao LIAN. State Prediction Method for Digital Twins of Power Distribution Networks by Integrating Temperature Factors with Wavelet LSTM[J]. Science Technology and Engineering, 2025 , 25 (7) : 2817 -2824 . DOI: 10.12404/j.issn.1671-1815.2403421
Year 2025 volume 25 Issue 7
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Article Info
doi: 10.12404/j.issn.1671-1815.2403421
  • Receive Date:2024-05-09
  • Online Date:2026-03-30
  • Published:2025-03-08
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  • Received:2024-05-09
  • Revised:2024-07-09
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    1 China Electric Power Research Institute, Beijing 100192, China
    2 State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
    3 Dongfang Electronics Co., Ltd., Yantai 264011, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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