Power data prediction method of new energy system based on wavelet transform and adaptive hybrid optimization

Abstract

Abstract New energy power generation is easily affected by climate, seasons, holidays and other factors, and has different fluctuation characteristics. Aiming at the problems of low prediction accuracy and weak generalization ability of power data with different characteristics, a new energy generation data prediction method based on wavelet transform and adaptive hybrid optimization is proposed. First, the power data is transformed by wavelets to obtain sequencing of different types of information. Then LSTM model and autoregressive moving average model are used to fit and predict different sequences. Finally, the prediction sequence is reconstructed to obtain the prediction result of the power data. This method takes the actual data of a power system as the data set, which not only has high prediction accuracy, but also has strong generalization ability.