Study on PV Power Prediction Based on VMD-IGWO-LSTM
DOI:
https://doi.org/10.13052/dgaej2156-3306.3936Keywords:
Photovoltaic power prediction, Gray wolf optimization algorithm, Long- and short-term memory neural networks, Variational modal decompositionAbstract
This research proposes a combined approach for predicting photovoltaic power by integrating variational modal decomposition (VMD), an improved gray wolf optimization algorithm (IGWO), and long- and short-term memory neural network (LSTM) techniques. The model takes into account the impact of varying environmental factors on photovoltaic power and aims to enhance prediction accuracy. Firstly, the four environmental factors constraining the PV output power are decomposed into eigenfunctions (IMFs) through variational modal decomposition; then the improved gray wolf optimization algorithm is used to optimize the long and short-term memory neural network; finally, the dimensionality-reduced dataset is inputted into the LSTM neural network, and the dynamic temporal modeling and comparative analysis on the multivariate feature sequences are carried out. The results show that the VMD-LSTM model optimized by the improved Gray Wolf algorithm predicts better than the comparison models LSTM, VMD-LSTM and VMD-GWO-LSTM, and achieves the accurate prediction of time-volt power in the external environmental changes.
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