Short-term PV Power Prediction Based on Similar Day Clustering with VMD-LTWDBO-BiLSTM
Aiming at the problems of fluctuation,intermittency and low prediction accuracy of photovoltaic power,the paper proposes a combined photovoltaic power prediction model based on the improved dung beetle optimization algorithm(LTWDBO)to find the optimality of penalty coefficients and decomposition layer parameters in the variational modal decomposition(VMD)as well as key parameters in the bidirectional long and short-term memory network(BiLSTM).Firstly,the Spearman correlation coefficient is used to select the main factors as inputs for K-means++similar day clustering and the historical data are divided into similar day samples of the different weather condition.Then the strong correlation features and PV power data under different weather conditions are decomposed by VMD,and the LTWDBO-BiLSTM prediction model is constructed for the sub-components.Finally,the predicted values are reconstructed by superposition to obtain the final prediction results.The simulation results show that the proposed VMD-LTWDBO-BiLSTM combined model has a significantly lower average absolute error under different weather conditions compared with other groups of models,verifying the better accuracy and robustness of the model under different weather conditions.
photovoltaic power generationpower predictionK-means++clusteringLTWDBOVMDBiLSTM