A method for predicting the joint probability density of output power of multi-wind farms includes such steps as establishing a prediction model of a sparse Bayesian learn
machine, predicting the probability density of output power of wind farms in multiple independent time periods in the future, predicting the probability density of output power of multi-wind farm, predicting the probability density of output power of sparse Bayesian
learning machine, predicting the probability density of output power of wind farm in multiple independent time periods in the future, predicting the probabilitydensity of output power of sparse Bayesian
learning machine, predicting the probability density of output power of multi-wind farm. The sparse Bayesian
learning machine is used to get the prediction error samples, and then the
correlation coefficient matrix between prediction errors is obtained according to the prediction error samples. The sparse Bayesian learning
machine is used to forecast themean and variance of wind farm output power, and the
covariance matrix is obtained by combining the mean and variance predicted with
correlation coefficient matrix, and the joint probability density prediction is completed. The method improves the accuracy and effectiveness of wind farm output power prediction by forecasting the output power of each period of wind farm and the correlation betweenthe output power of each period of wind farm, makes the prediction more close to the actual situation of the real wind farm, and provides more abundant and accurate information for the dispatching decision-making of the power
system with wind farms.