Forecast error extreme value analysis method, device, computer equipment and storage medium

Abstract

The invention provides a prediction error extreme value analysis method and device, a computer device and a readable storage medium. The method comprises: acquiring a wind power prediction errors, numerical weather forecast data and historical anemometry data, dividing the wind power prediction errors according to the corresponding relationship between the preset conditions and the wind power prediction errors, to obtain a plurality of blocks, obtaining the block maximum value in each block, selecting a concomitant variable according to the numerical weather forecast data, the historical anemometry data and the maximum value of each block, and obtaining the extreme value distribution of the wind power prediction errors through the concomitant variable and the maximum value of each block. By adopting the method, the extreme values of the day-ahead wind power prediction errors are analyzed, thus the accuracy of extreme value modeling is increased, and the scheduling availability of the day-ahead prediction result is improved.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a prediction error extreme value analysis method, device, computer equipment and storage medium. Background technique [0002] With the increasingly serious problems of environmental pollution and energy shortage, the proportion of wind power in the total power generation of the power system has increased year by year due to its advantages of abundant resources, clean and pollution-free, small actual land occupation, and renewability. Since wind power is an unstable energy source with random fluctuations, a series of problems brought to the power system have become increasingly prominent and have seriously affected the development of the wind power industry. Therefore, timely and accurate prediction of wind power power is required to solve this problem. At present, the main application of wind power forecasting in scheduling is focused on the time scale within days....

AI Technical Summary

Problems solved by technology

[0005] Based on this, it is necessary to provide a prediction error extreme value analysis method and device for the problem of low scheduling availability of day-ahead prediction results when analyzing all wind power prediction errors in local periods through the probabilistic prediction method of the full domain in the traditional technology , computer equipment and storage media

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