Study on ACO-VMD-LSTM-based Ultra-short Term Prediction of Photovoltaic Power
Aiming at improving the low accuracy of conventional ultra-short term algorithms for photovoltaic power pre-diction,this paper proposes a prediction model based on modified variational mode decomposition and short-term memory network.First the influencing factors of photovoltaic power are analyzed using Pearson correlation coefficient.Second the photovoltaic power sequence is decomposed using an ant colony algorithm-based variational mode decomposition,and the predicted power is obtained by input meteorological factors at all independent modal component levels into long-term and short-term memory network.The proposed model is shown by simulation results more accurate than BPNN and LSTM models,and may be useful in predicting powers of photovoltaic generations.
photovoltaic powerultra-short term predictionant colony optimization algorithmlong and short term memory network
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