Short-term photovoltaic power prediction method based on optimized DBN and Bi-LSTM

The invention discloses a short-term photovoltaic power prediction method based on optimized DBN and Bi-LSTM, and the method comprises the steps: building an SSA optimized DBN and PSO optimized Bi-LSTM combined prediction model through employing the characteristic that the DBN can capture nonlinear...

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Bibliographische Detailangaben
Hauptverfasser: SONG LI, LIU GANG, WANG SHUNJIANG, LI DIANYANG, WANG DUO, LING ZHAOWEI, ZHOU GUIPING
Format: Patent
Sprache:chi ; eng
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Zusammenfassung:The invention discloses a short-term photovoltaic power prediction method based on optimized DBN and Bi-LSTM, and the method comprises the steps: building an SSA optimized DBN and PSO optimized Bi-LSTM combined prediction model through employing the characteristic that the DBN can capture nonlinear features and the capability of the Bi-LSTM for processing time sequence features, completing the deep feature extraction of a training data sequence, carrying out the parameter optimization of the DBN through employing the SSA, and carrying out the prediction of the short-term photovoltaic power of the DBN. The DBN convergence speed and the feature extraction capability are improved, and the generalization performance is improved; according to the method, the hidden layer unit number alpha, the learning rate epsilon and the discarding probability rho in the Bi-LSTM are adjusted through PSO, the network robustness and the global search capability are improved, and the convergence speed is increased. 本发明公开了一种基于优化DBN和