Wind power integration prediction method based on multi-source numerical value weather forecast

Abstract

The invention relates to a wind power integration prediction method based on multi-source numerical value weather forecast. The method includes the following steps: a first step of pre-processing prediction data; a second step of establishing a wind power prediction model; a third step of determining linear optimization combination parameters, establishing a linear regression model; a fourth step of predicting future time wind power. According to the invention, the method obtains a group of wind power prediction results based on the multi-source numerical weather forecast and conducts combination optimization on the results by using multiple linear regression so as to provide prediction result which is used for guiding the certainty of production and operation for grid scheduling departments.

Description

technical field [0001] The invention relates to a method for wind power aggregate prediction, in particular to a method for wind power collective prediction based on multi-source numerical weather forecasting. Background technique [0002] Predicting the output power of wind farms and incorporating wind power into the dispatching plan of the power grid is one of the effective measures to deal with the challenges brought by large-scale wind power to the production and operation of the power system. At present, wind power forecasting systems have been widely used in major wind power countries in the world, such as the United States, China, and Germany, and have become an important support system for wind power optimal dispatching. Relevant studies have shown that predicting the output power of wind farms and turning unknown wind power output into basic knowledge is conducive to taking countermeasures in advance and improving the safety and reliability of the power grid. It is...

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Problems solved by technology

[0013] However, numerical weather prediction, which is the main input data and the main source of errors, involves multiple parameters in its model, and a small change in a certain parameter will also cause a large difference in the final forecast result. Therefore, a numerical weather prediction model may only be Good at a certain weather phenomenon, or have advantages on different time scales, in most cases, it brings more than 2 times the error of the forecasting algorithm

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