Multi-step wind power forecasting method based on singular spectrum analysis and locality sensitive hashing

Abstract

The invention discloses a multi-step wind power forecasting method based on singular spectrum analysis and locality sensitive hashing. The method is charactierzed by decomposing historical wind powerdata of a wind power plant into two independent components through singular spectrum analysis, the independent components being a low-frequency average trend component for reflecting wind energy overall change trend and a high-frequency fluctuation component for reflecting intermittency and fluctuation of wind respectively; reconstructing the two components in a phase space to obtain an average trend section and a fluctuation component section; and finding similar average trend sections of an average trend section to be forecasted through locality sensitive hashing, and carrying out locality predication. To prevent accumulated errors brought by separate forecast of each component and fixed error brought by forecast of only one component, the forecast input is combination of the similar average trend sections and corresponding fluctuation component sections, and finally, a prediction result of wind power output power is obtained. The method is clear in physical significance in wind power plant generation power prediction, short in prediction time and accurate and stable in prediction results; and the prediction results do not rely on prior knowledge of users.

Description

technical field [0001] The invention relates to the technical field of wind power generation power prediction, in particular to a multi-step wind power prediction method based on singular spectrum analysis and local sensitive hashing. Background technique [0002] With the scarcity of resources and the growing call for environmental friendliness of human beings, the development and utilization of wind energy has been paid more and more attention. However, due to its strong randomness and intermittent nature, it brings great difficulties to wind energy prediction, which directly limits the application of wind energy in large power grids. It is of great significance to carry out wind energy forecasting. First of all, for wind farms, the evaluation and prediction of wind energy is an important part of the work to assess whether large-scale wind power projects are feasible. Furthermore, for the entire power system, once the wind energy of the wind farm and the output of the ge...

AI Technical Summary

Problems solved by technology

[0004] 1. The prediction accuracy of the existing prediction models largely depends on the prior knowledge of the user, and the predictions have relatively large limitations;

[0005] 2. The inherent laws of wind energy changes and the physical meaning or characteristics reflected in the data have not been fully explored;

[0006] 3. The prediction accuracy is not high, the prediction time is long, and the stability needs to be strengthened

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