Wind power and load temporal dependence model based on dynamic Bayesian network

This article regards the dependency structure of wind power and load time series at each moment as the conditional probability of a high-dimensional discrete stochastic process. A dynamic Bayesian network (DBN) model is proposed to calculate the joint probability distribution of high-dimensional stochastic processes, which can completely describe the potential dependency structure of wind power and load at each time. The DBN model is based on a data-driven approach, using Bayesian information criteria (BICs) as the scoring function, and using a greedy search algorithm to determine the initial network and transmission network DBN structure. Maximum likelihood estimation (MLE) is used to estimate the parameters of the initial network and the transmission network. The DBN model is established from these two networks, and the joint probability distribution of the stochastic process is obtained. Sequential Monte Carlo (SMC) sampling is performed on the DBN model to generate the dependent simulation sequence of wind power and load. Using probability density, mean value, maximum output fluctuation, and autocorrelation and cross-correlation functions as evaluation indicators, the statistical characteristics of the historical sequence, simulation sequence based on the DBN model, and simulation sequence based on the hidden Markov model (HMM) are comprehensively compared and analyzed to verify the effectiveness of the DBN model.