43 results about "Heteroscedasticity" patented technology

The invention discloses a joint probability density prediction method of short-term output power of a plurality of wind power plants. The method comprises the following steps: carrying out single point value prediction on output power of each wind power plant by using a support vector machine regression prediction model; building a sparse bayesian learning model as to a prediction error to carry out probability density prediction of the error, so as to obtain an expected value and a variance of marginal probability density function prediction of the output power of a single wind power plant; carrying out statistic analysis on the prediction error characteristics of the output power of the plurality of wind power plants, building a dynamic conditional correlation-multivariate generalized autoregressive condition heteroscedasticity model, and integrating a marginal probability density prediction result of the output power of the single wind power plant and a correlation coefficient matrix to obtain a joint probability density function of the output power of the plurality of wind power plants; forming a multidimensional scene including space-time correlation characteristics by using a sampling technique. By adopting the joint probability density prediction method, a mean prediction value and prediction uncertainty information of the output power of the single wind power plant can be provided; the dynamic space-time correlation characteristics between output power prediction of the plurality of wind power plants also can be quantitatively described.

A computer-implemented method includes employing a dynamic Boltzmann machine (DyBM) to predict a higher-order moment of time-series datasets. The method further includes acquiring the time-series datasets transmitted from a source node to a destination node of a neural network including a plurality of nodes, learning, by the processor, a time-series generative model based on the DyBM with eligibility traces, and obtaining, by the processor, parameters of a generalized auto-regressive heteroscedasticity (GARCH) model to predict a time-varying second-order moment of the times-series datasets.

The invention discloses a method for extracting a reservoir dispatch function based on joint probability distribution. By collecting basic reservoir data and long series of measured runoff data, the reservoir is deterministically optimized and dispatched to obtain an optimal dispatch process sample series, and decision variables and state variables are determined. Then use the Copula function to construct the joint probability distribution function of the decision variable and the state variable, and then infer the conditional probability distribution function of the decision variable when the state variable is given, and perform the extraction and uncertainty analysis of the reservoir dispatching function. The reservoir dispatching function extracted by the invention can accurately capture the inherent nonlinear and heteroscedastic correlation structure between decision variables and state variables, and the calculated confidence interval estimation value can quantitatively evaluate the uncertainty of reservoir dispatching, which provides useful information for decision-making risk analysis. reference basis.