Research on Photovoltaic Power Generation Prediction Method Based on Improved DBO-BiLSTM-GRU
Due to the influence of multiple factors on the magnitude of photovoltaic power generation,it has obvious characteristics such as volatility,instability,and seasonality.Therefore,how to accurately predict photovoltaic power generation is currently a major challenge.To solve the above problems,a photovoltaic power prediction method based on improved DBO-BiLSTM-GRU is proposed.Among them,the bidirectional long short term memory network(BiLSTM)is used to extract temporal features of historical photovoltaic power generation data.Firstly,use a more streamlined gated recurrent unit(GRU)to further predict photovoltaic power generation.Secondly,the improved dungeon optimization algorithm(DBO)is introduced to optimize the hyperparameters of the BiLSTM-GRU combination model,further improving the prediction efficiency and accuracy of the model.Finally,the model was validated using real power generation data from photovoltaic power stations in Anhui Province.The results indicate that the proposed prediction model can significantly improve prediction accuracy compared to commonly used advanced prediction models.
power systemphotovoltaic power generationgeneration predictionoptimization algorithm
万方数据
维普
