Short-term combination forecasting method for wind power

Abstract

A short-term combination forecasting method for wind power comprises the steps that (1) normalization is performed on wind speed and wind power data, and support vector machine regression, an Elman neural network and a BP neural network are respectively utilized to establish corresponding single forecasting models; (2) staging is performed on forecasting results obtained by training all the single forecasting models according to the magnitude of the wind speed; (3) parameters to be optimized are selected, and a combination forecasting model is established; (4) an objective function is determined according to the combination forecasting model, a constraint condition that the mean absolute percentage error minimum serves as the objective function is adopted, and optimized parameters are obtained; (5) all stages of weight coefficient values after staging are obtained according to the optimized parameters, and the combination forecasting model is updated; (6) the corresponding weight coefficient values are dynamically selected according to the magnitude of the wind speed, and the wind power test data are utilized to train and forecast the updated combination forecasting model to obtain a combination forecasting value. According to the short-term combination forecasting method for the wind power, the advantages of all the single forecasting models are effectively synthesized, forecasting risks are lowered, and forecasting accuracy is high.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, in particular to a short-term combination forecasting method of wind power based on entropy discrimination bee colony algorithm optimization. Background technique [0002] In recent years, wind energy, as a renewable energy, has developed rapidly around the world. As of December 2012, the world's installed wind power capacity has increased from 60GW in 2000 to 282.578GW, and it is expected that the world's installed wind power capacity will reach 460GW by 2015. With the rapid development of wind power, grid connection has become a research hotspot for making full use of wind power. The output power of wind power depends on the wind speed. However, due to the uncertainty and intermittency of wind speed, it is bound to have a serious impact on the stability of the power grid. [0003] Accurate short-term forecasting of wind power is conducive to the power sector to formulate reason...

AI Technical Summary

Problems solved by technology

The output power of wind power depends on the wind speed. However, due to the uncertainty and intermittency of wind speed, it is bound to have a serious impact on the stability of the power grid.

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