A method and system for short-term wind pow prediction

Abstract

The invention provides a short-term wind power forecasting method and system, comprising: collecting forecasting meteorological data; Inputting the forecasted meteorological data into a short-term wind power forecasting model created in advance to obtain a wind power forecasting value; The training data set of the short-term wind power prediction model is obtained by taking historical meteorological data related to the total output power of the wind turbine as modeling data and extracting the modeling data by PCA principal component. The technical proposal provided by the invention improves the operation speed and the modeling accuracy.

Description

technical field [0001] The invention relates to wind power prediction, in particular to a short-term wind power prediction method and system. Background technique [0002] At present, wind power, as a clean energy, has become the third largest electric energy after thermal power and hydropower. However, the intermittent, random and fluctuating characteristics of wind speed make it impossible to adjust flexibly like thermal power, which brings challenges to the safe and stable operation of the power system. With the continuous expansion of wind power scale and the continuous growth of installed capacity, wind power consumption has become an important issue affecting the healthy and sustainable development of the wind power industry, and the issue of wind curtailment is more prominent. As an effective means to increase wind power access capacity and improve the safety and economy of power system operation, wind farm power prediction has become a research hotspot in recent yea...

AI Technical Summary

Problems solved by technology

[0004] At present, the main problem facing the field of wind power forecasting is that the forecasting accuracy is not high

For the NWP prediction model, the main problem is that the meteorological data fluctuates greatly and the working conditions are complex, which leads to the insufficient generalization ability of the prediction model

[0005] At present, there is still a large gap in the field of short-term wind power forecasting. In practical applications, it is affected by many aspects, including: 1. The influencing factors of short-term wind power forecasting and the degree of influence on wind power have not yet been clearly defined; 2. There are many types of characteristic quantities of factors affecting wind power forecasting, a large amount of information, and complex data processing; 3. The accuracy of wind power intelligent forecasting needs to be improved

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