Wind power prediction method

Abstract

The invention discloses a wind power prediction method. The method comprises the following steps: collecting sample data and processing; establishing a BP (Back Propagation) neural network model by using the processed sample data, and training the BP neural network model to obtain a final weight, a threshold value and a relative error sequence of a prediction value relative to a sample value; solving an initial prediction value of output power according to the weight and the threshold value after the training is completed; calculating a calculation relative error state corresponding to the initial prediction value of the output power by using a markov chain error correction model; combining the initial prediction value of the output power with the corresponding calculation relative error state to calculate corrected power. According to the method, the accuracy of wind power prediction is further improved, and an accurate wind power prediction value is provided for daily power generation scheduling and secure economic dispatch of a wind power generation power grid.

Description

technical field [0001] The present invention relates to the field of power forecasting, and more specifically relates to a wind power forecasting method. Background technique [0002] Due to the increasingly serious problems of environmental pollution and energy shortage, wind power has attracted widespread attention due to its advantages such as abundant resources, clean and pollution-free, small actual land occupation, and renewability. However, as an unstable energy source, wind energy is random, intermittent and uncontrollable. With the development of wind power, the penetrating power of wind farms continues to increase, and grid-connected wind power increases the difficulty of formulating power system dispatching plans. [0003] Due to the imperfection of my country's current wind power forecasting system, the lack of some basic data, and the lack of accuracy, it is very difficult to accurately predict wind power power. Therefore, it is impossible to accurately formula...

AI Technical Summary

Problems solved by technology

[0003] Due to the imperfection of my country's current wind power forecasting system, the lack of some basic data, and the lack of accuracy, it is very difficult to accurately predict wind power power. Therefore, it is impossible to accurately formulate a system scheduling plan after wind power is connected to the grid, and it is also impossible to arrange a reasonable operation mode.

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