Probabilistic prediction of wind power based on improved Bayesian neural network

Abstract

Deterministic wind power prediction can be used for long time-scale optimization of power dispatching systems, but the probability and fluctuation range of prediction results cannot be calculated. A Bayesian LSTM neural network (BNN-LSTM) is constructed based on Bayesian networks by placing a priori distributions on top of the LSTM network layer weight parameters. First, the temporal convolutional neural network (TCNN) is used to process the historical time-series data for wind power prediction, which is used to extract the correlation features of the time-series data and learn the trend changes of the time-series data. Then, the mutual information entropy method is used to analyze the meteorological dataset of wind power, which is used to eliminate the variables with small correlation and reduce the dimension of the meteorological dataset, so as to simplify the overall structure of the prediction model. At the same time, the Embedding structure is used to learn the temporal classification features of wind power. Finally, the time series data processed by TCNN, the meteorological data after dimensionality reduction, and the time classification feature data are fed into the BNN-LSTM prediction model together. Compared with a Bayesian neural network, continuous interval method, and Temporal Fusion Transformer (TFT), which is one of the most advanced time series prediction networks, the improved BNN-LSTM can respond more accurately to wind power fluctuations with better prediction results. The comprehensive index of probability prediction of pinball loss is smaller than those of the other three methods by 53.2%, 24.4%, and 11.3%, and the Winkler index is 3.5 %, 34.6 %, and 8.2 % smaller, respectively.