WOA-QRLSTM reservoir inflow probabilistic prediction method based on mean shift clustering
A technology of inflow and mean shift, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems of difficulty in obtaining reservoir inflow forecast results, low forecast accuracy, and unsatisfactory forecast effects. Quantify uncertainty, improve accuracy and stability, improve the effect of accuracy
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[0026] In this embodiment, a method based on mean drift clustering and WOA-QRLSTM predictive model is predictive, such as figure 1 As shown, it is performed as follows:
[0027] Step 1. Collect the reservoir warehousing traffic data and affect the factor data of the reservoir's storage traffic and preprocess, resulting in the processing data set DataSet = {[w (t), g m (t)] | T = 1, 2, ..., t; m = 1, 2, ..., m}, where W (t) represents the reservoir flow rate of the T date; M represents the reservoir Number of factors of the library flow, g m (t) Indicates the value of the mth impact factor in the dayt day; T represents the total number of collected days;
[0028] Step 2, at a time interval at a certain time period D, divide the data set after the pre-treated data, to obtain I group sample data {dataGRuop i | i = 1, 2, ..., i}, and I meet [T / D], where DataGroup i Indicates Group I sample data, and DataGroup i = [W '(i), g' m (i)], g ' m (i) = (g m (D × (i-1) +1), g m (D × (i-1) +2...
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