Power generation prediction of small hydropower station based on EEMD-LSTM
Accurate and effective power generation prediction is of great significance to the economic operation of smart grids.In this paper,a model that combines the ensemble empirical mode decomposition method to construct a long short-term memory network was proposed to predict the power generation of small hydropower stations.The ensemble empirical mode decomposition is used to perform feature decomposition on the historical power generation time series of the hydropower station to obtain power generation components containing different characteristics.The decomposed historical power generation time series and related influencing factors(river runoff,guide vane opening,month)are used as the input of the model,and each parameter in the model is adjusted through training,and then the prediction results are superimposed to obtain the final predicted power generation.After experimental comparative analysis,it was found that the long-short-term memory network prediction model constructed by combining the ensemble empirical mode decomposition method has higher accuracy than the traditional long-short-term memory network prediction model,and verified that the decomposition-prediction-reconstruction method can generate electricity in hydropower stations.It also provides scientific basis and decision-making guidance for large-scale grid connection of new energy and overall economic operation of the power grid.
万方数据
维普
