Research on Inflow Forecasting Model of Non-Ecological Factors Based on RIME-VMD-SSA-LSTM Method
For the water inflow prediction problem with a large number of upstream and downstream power plant communities,using traditional hydrological models to predict the inflow flow of reservoirs has a significant error.The flow measurement point information contains a large number of nonlinear influencing factors.Taking the flood season inflow data of Lubuge Power Station as an example,RIME-VMD-SSA-LSTM combination algorithm is used to study the short-term inflow prediction model of reservoirs with strong non ecological interference in the upstream.The results show that the traffic prediction model proposed based on this algorithm has good performance in four evaluation indicators(RMSE=8.8743,MAE=6.3193,MAPE=3.5335%,R2=0.98631),and the maximum prediction error is controlled within 50%.Compared to LSTM and VMD-LSTM algorithms,it can more accurately predict short-term water inflow under strong non ecological interference.
deep learningshort term water inflow predictionVMD-LSTMprediction accuracy
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