Semiconductor Optoelectronics, Volume. 46, Issue 3, 536(2025)
Simulated Annealing-Long Short-Term Memory Based Light-Power Prediction Method for Power Cables
To improve the accuracy of predicting anomalies in optical cable systems based on optical power data, in this study, we employ the simulated annealing-long short-term memory (SA-LSTM) algorithm and predict optical power fluctuations. First, the raw optical power data are normalized. Subsequently, the SA algorithm is used to perform global optimization of the LSTM algorithm’s key parameters, including the number of iterations, hidden layer units, and learning rate, across a wide range. These optimized parameters are used to construct an LSTM model that can predict future trends in optical power. Experimental results demonstrate that the optical power, predicted using the proposed method with optimal parameters, exhibits a root-mean-square error of 2.83 × 10−3 dB, which is only 0.14% of the mean value. The prediction accuracy of the proposed method is 91.7%, 91.0%, 96.4%, and 96.3% higher than those of the autoregressive moving-average radial-basis-function, autoregressive integrated moving-average support-vector-machine, autoregressive moving-average LSTM, and autoregressive integrated moving-average LSTM algorithms, respectively. Thus, the proposed method can predict optical power trends in power cable systems with high accuracy, showing significant potential for engineering applications.
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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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Received: Jan. 22, 2025
Accepted: Sep. 18, 2025
Published Online: Sep. 18, 2025
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