Short-term wind speed prediction method of Gaussian process regression and particle filtering

Abstract

The invention discloses a short-term wind speed prediction method of Gaussian process regression and particle filtering, thereby realizing on-line dynamic detection and correction of abnormal values and improving wind speed prediction accuracy. According to the method, an input variable set having the highest correlation with a wind speed value at a to-be-predicted time is determined by using a partial autocorrelation function, a state vector is determined, and a proper training sample set is constructed; a Gaussian-process-regression-based short-term wind speed prediction model is establishedin the training sample set and a fitting residue during the training process is given; on the basis of combination of the state vector and the Gaussian process regression model, a particle filteringstate space equation is established and state estimation is carried out on a current measurement value by using a particle filtering algorithm; and the estimation value and the measurement value residual of particle filtering are analyzed, determination is carried out based on a 3 sigma principle, and an abnormal value is corrected. According to the method provided by the invention, the abnormal value can be detected and corrected effectively; the short-term wind speed prediction precision is improved; and a wind speed prediction problem of the power system is solved.

Description

technical field [0001] The invention relates to a short-term wind speed prediction method of a power system, which is used to predict the wind speed of the power system and belongs to the technical field of power systems. Background technique [0002] At present, there are mainly two types of methods for wind speed prediction: numerical weather prediction and statistical models. Numerical weather prediction needs to establish a physical model, and obtain information such as wind speed, wind direction, temperature and humidity through microscopic meteorology theory and computational fluid dynamics. The statistical model method mainly adopts the idea of ​​mathematical statistics, and predicts by mining the inherent laws existing in the data. Such methods mainly include time series, neural network, support vector machine, Kalman filter, etc. Due to the typical nonlinear, strong fluctuation and strong randomness characteristics of wind speed, it is difficult to describe the ch...

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Problems solved by technology

[0005] Purpose of the invention: Aiming at the problem that the noise or lack of data in the historical wind speed sequence affects the accuracy of wind speed prediction, the present invention provides a short-term wind speed predic

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