Wind power prediction method and system

Abstract

The invention provides a wind power prediction method and system, and the method comprises the following step: (A), obtaining a historical data set which comprises the wind speed and wind power, and carrying out the preprocessing of the historical data set; (B), training an artificial neural network prediction model through the historical data set; (C), predicting the wind power based on the trained artificial neural network prediction model and the inputted wind speed data. According to the invention, the method employs the historical data comprising the wind speed and the wind power, achieves a wind power prediction model through a method combining a genetic algorithm and a back propagation neural network, achieves the precise prediction of the wind speed data, and improves the prediction efficiency of the wind power.

Description

technical field [0001] The technical field of wind power of the present invention, in particular, relates to a wind power prediction method and system. Background technique [0002] As wind power is widely used by humans, the penetration rate of wind power resources in the power system is also increasing. Wind energy is one of the renewable energy sources with the lowest power generation cost and the most available resources, and expanding its coverage in power generation can Dramatically reduce greenhouse gas and air pollution from burning fossil fuels such as natural gas and coal when using electricity obtained from conventional power generation systems, wind power technology is now a major contributor to the growing global clean electricity market Accurate and reliable wind power forecasting systems It is widely considered to be the main reason for the increase in wind power penetration. However, because wind power can only be used in sufficient windy weather, and is aff...

AI Technical Summary

Problems solved by technology

However, because wind power can only be used in sufficient windy weather, and is affected by various factors such as seasons, terrain, temperature, air pressure, etc., the generation of wind is random, and wind power is intermittent and uncertain. Therefore, The Accuracy of Wind Power Forecasting Technology Is Increasingly Important

[0003] At present, most of the existing technologies use numerical weather prediction (NWP) for correction. This method usually requires a lot of physical considerations to achieve the best prediction accuracy, and the amount of calculation is large, and the use of historical data is insufficient, and it relies heavily on numerical weather prediction. (NWP) Accuracy

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