Wind speed prediction is of great importance for the control of wind power generation. For the problem of insufficient accuracy in wind speed prediction, this paper proposes a Multi-Network Parallel Integration Model (MNPIM), which is a combination of LSTM, GRU, RNN, ANN and a differential evolution algorithm to assign weights to the output of each sub-model in order to improve the accuracy of wind speed prediction. algorithm is used to assign weights to the output of each sub-model in order to improve the accuracy of wind speed prediction. The modelling and prediction of wind speed of a wind farm in Gansu Province is taken as an example, and the cross-validation method is introduced to partition the data set and compare with the existing LSTM, GRU, RNN, LR and GPR prediction methods.
Highly accurate runoff forecasting is essential for the efficient use of water resources. Considering the non-linearity and randomness of runoff sequences, a selective ensemble forecasting method combined with the PSO algorithm is proposed. Firstly, sub-learners are selected for homogeneous ensemble and the parameters of each sub-learner are rate-determined on the training set. Then, the PSO algorithm is used to assign weights based on the performance of the sub-learners on the validation set to obtain the selective ensemble model. Finally, the selective ensemble method was validated on the test set. Experiments were performed using runoff data from the WuLong station in the Yangtze River basin, and the results show that the selective ensemble method can provide more accurate forecast results than homogeneous ensemble with the same average weights of the learners.
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