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Research on Power Quality Prediction Based on BiLSTM Optimized by Bayesian Algorithm

The steady-state indexes of power quality are obviously nonlinear and non-stationary under the influence of many factors, leading to the existing algorithms not to achieve high-precision prediction for power quality. Considering the excellent performance of Bi-directional Long Short-Term Memory model in time series analysis and the outstanding advantages of Bayesian algorithm in global optimization, a prediction model for power quality based on BiLSTM optimized by Bayesian is proposed. The actual data of power quality are used to test the performance of the model, and the results show that the proposed prediction model has higher accuracy and better applicability.