Abstract: With the rapid development and wide popularization of new energy generation technology,it has become a key part of the power system.Among them,the accurate prediction of new energy generation power is of great significance for the rational planning of power system.However,the existing new energy generation power prediction methods still have the following challenges:1)The hyperparameters of the prediction model based on deep neural network have an important impact on the prediction performance of the model,and most of the current algorithms still use the artificial method to assign the hyperparameters.2)It is difficult for the existing prediction models to efficiently mine the long-term dependencies in the time series data,thus affecting the prediction accuracy.To solve these problems,this paper proposes a CSO-LSTM(competitive swarm optimizer and long short-term memory) based method for the prediction of new energy generation power,which aims to use a two-stage model to comprehensively improve the prediction performance.Firstly,in the first stage of the model,a LSTM hyperparameter optimization algorithm based on competitive group optimization is proposed,which uses the good exploration ability and global optimization ability of competitive group optimization algorithm to realize the adaptive adjustment of the hyperparameters of the prediction model.Then,in the second stage of the model,a LSTM model based on the combined multi-gating mechanism is designed,which combines the self-attention gating mechanism and the combined multi-gating network to mine the long-term dependencies in the new energy generation time series data,so as to further adapt to the new energy generation patterns at different time scales.Finally,the proposed CSO-LSTM is compared with four advanced prediction methods on two real datasets and one simulation dataset,and the experimental results verify the effectiveness and efficiency of the proposed CSO-LSTM model.

Key words: Competitive swarm optimization, Long-short term memory network, Power prediction of new energy generation, Multi-scale time series data mining, Parameter optimization