Real-time wind power prediction and error analysis method based on mixture Gaussian distribution

Abstract

The invention provides a real-time wind power prediction and error analysis method based on mixture Gaussian distribution.The real-time wind power prediction and error analysis method is characterized by including the steps of data acquisition and processing, mixed Gaussian distribution model establishment, calculation of mixed distribution function parameters and model evaluation based on an expectation maximization algorithm and the like, linear combination is conducted on multiple Gaussian distributions to obtain a 'multi-weight mixed Gaussian distribution model', model parameters are estimated by adopting the expectation maximization algorithm, and the computational complexity of the maximum likelihood estimation is reduced; modeling is performed by directly relying on the objective law of error distributions themselves, man-made subjectivity is avoided, the shortcoming that a single distribution model is inflexible in shape and poor in universality is overcome, and more accurate description to real-time wind power prediction errors is achieved.The real-time wind power prediction and error analysis method has the advantage of being scientific, reasonable, good in applicability, high in prediction accuracy and the like.

Description

technical field [0001] The invention relates to the technical field of wind power, and relates to a method for analyzing errors in real-time prediction of wind power based on mixed Gaussian distribution. Background technique [0002] In recent years, with the continuous improvement of wind power installed capacity and penetration rate in the power system, wind power prediction methods have attracted more and more attention from researchers at home and abroad. Forecasting methods have always been considered to be the most important way to improve the accuracy of wind power forecasting. Relevant scholars at home and abroad have done a lot of work, such as time series method, phase space reconstruction method, artificial neural network method, combined forecasting method, non-parametric estimation method, etc. method, support vector machine method, etc. The change of wind power does not have obvious regularity, so the current wind power prediction accuracy is still limited, so...

AI Technical Summary

Problems solved by technology

In the actual prediction, the error distribution presented by a large number of samples may be relatively close to the normal distribution, but there are large differences due to the difference between the prediction method and the actual environment. Therefore, a general method should be proposed for different prediction methods and different wind farm scales. method

Images