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Method for estimating wind power forecasting error burst based on hidden markov model

A technology for wind power prediction and error intervals, which is applied in computing, electrical digital data processing, and special data processing applications. It can solve the problems of not considering the timing correlation of wind power prediction errors, so as to improve the wind power grid-connected capacity and realize the acceptance horizontal effect

Inactive Publication Date: 2017-11-17
DALIAN UNIV OF TECH
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Problems solved by technology

The results obtained by the existing methods do not consider the time series correlation between the wind power prediction errors in adjacent periods, and do not use the probability matrix to describe the change trend of the wind power prediction errors

Method used

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  • Method for estimating wind power forecasting error burst based on hidden markov model
  • Method for estimating wind power forecasting error burst based on hidden markov model
  • Method for estimating wind power forecasting error burst based on hidden markov model

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Embodiment Construction

[0052] the following to figure 2 The data shown is taken as an example, and the specific implementation manner of the present invention is described in combination with the technical solution. Book figure 2 The actual wind power data of a certain province in my country is used for analysis. The sampling interval of the data is 15 minutes. The dotted line part is the predicted value of wind power power four hours in advance, and the solid line part is the actual output value of wind power power. Taking N=4, m=4, and n=16 as parameters, starting from the data time node 1001, the error intervals of 768 time nodes in total are estimated continuously for 8 days.

[0053] figure 1 It is the flow chart of wind power error interval estimation, and the specific steps are as follows:

[0054] The first step is to model the wind power period forecast error.

[0055] Firstly, according to the formulas (5) and (6), the forecast error data of the wind power M time nodes before the cur...

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Abstract

The invention belongs to the field of electric system forecasting, and provides a method for estimating a wind power forecasting error burst based on a hidden markov model. A wind power ultrashort term forecasted value reported to a dispatching department for a wind power plant is a deterministic point forecasting and given in a curved mode, but the problem that the forecasting accuracy is not high exists. By introducing an HMM model, modeling is performed on the wind power ultrashort term forecasting error, the error burst is processed by means of a locally weighted regression scatter plot smoothing method, the result accuracy is improved and the result conservation is lowered. The wind power forecasting error burst can be obtained, an error fluctuation state transition matrix can be obtained, and references are provided for dispatching operation.

Description

technical field [0001] The invention belongs to the field of power system forecasting, and relates to a method for analyzing and predicting the super-short-term forecast value of wind power reported by a wind farm to the dispatching department during the dispatching process of the power system. scope and trends. Background technique [0002] With the continuous depletion of fossil energy and the intensification of environmental problems, the utilization rate of renewable energy represented by wind power continues to increase. Driven by various countries, the commercialization of wind power continues to increase, the technology is gradually mature, and has been rapidly developed. develop. At present, the total installed capacity of wind power in my country ranks first in the world, and the continuous increase in the scale of wind power grid-connected has alleviated the problem of energy shortage in my country to a certain extent. However, due to the obvious randomness and v...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 周玮钟佳成
Owner DALIAN UNIV OF TECH
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