Semiconductor Optoelectronics, Volume. 46, Issue 3, 536(2025)

Simulated Annealing-Long Short-Term Memory Based Light-Power Prediction Method for Power Cables

CHEN Jinshan1, WU Shiyu2, LIN Guodong2, WANG Xinlan2, LIN Shu3, LI Zhaoxiang2, and ZHAO Lijuan4
Author Affiliations
  • 1State Grid Fujian Electric Power Co., Ltd., Fuzhou 350001, CHN
  • 2State Grid Fujian Electric Power Co., Ltd. Electric Power Research Institute, Fuzhou 350007, CHN
  • 3State Grid Fujian Electric Power Co., Ltd. Xiamen Power Supply Company, Xiamen 361004, CHN
  • 4Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, CHN
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    References(4)

    [9] [9] Li T, Hua M, Wu X. A hybrid CNN-LSTM model for forecasting particulate matter (PM2.5)[J]. IEEE Access, 2020, 8: 26933-26940.

    [10] [10] Sang S, Li L. A novel variant of LSTM stock prediction method incorporating attention mechanism[J]. Mathematics, 2024, 12(7): 945.

    [12] [12] He D, Qu Y, Sheng G, et al. Oil production rate forecasting by SA-LSTM model in tight reservoirs[J]. Lithosphere, 2024, 2024(1): 1-15.

    [13] [13] Tang J, Xu L, Wu X, et al. A short-term forecasting method for ionospheric TEC combining local attention mechanism and LSTM model[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1001305.

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    CHEN Jinshan, WU Shiyu, LIN Guodong, WANG Xinlan, LIN Shu, LI Zhaoxiang, ZHAO Lijuan. Simulated Annealing-Long Short-Term Memory Based Light-Power Prediction Method for Power Cables[J]. Semiconductor Optoelectronics, 2025, 46(3): 536

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    Paper Information

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    Received: Jan. 22, 2025

    Accepted: Sep. 18, 2025

    Published Online: Sep. 18, 2025

    The Author Email:

    DOI:10.16818/j.issn1001-5868.20250122001

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