Research on new energy power prediction technology based on privacy protection

New energy power prediction is an important part of the transition process from the traditional power system to the new power system. How to improve the power prediction accuracy while ensuring that data privacy is not leaked is an issue that needs to be focused on. Based on this, this paper constructs a new energy power prediction model integrating NGBoost and LSTM by screening the optimal feature sequences as model inputs, then encrypting the transmission aggregation process of model parameters and finally testing and evaluating the scheme based on a real data set. Experiments show that the scheme proposed in this paper not only improves data confidentiality to a certain extent compared with a single prediction model, but also the model is characterized by robustness and high prediction accuracy.