Short-term wind power forecasting method based on gsa-lssvm model

Abstract

The invention discloses a GSA-LSSVM model-based short period wind electricity generation power prediction method. Complex original wind speed-power data is reasonably classified into three types according to wind speed climbing gradients, a defect of an excessive calculation amount of a model can be made up for, characteristic features of the classified data can be reinforced, a conversion relation between wind speed and power can be well demonstrated, a phenomenon that the same speed corresponds to different power can be alleviated, an improved GSA can help alleviate a defect that a conventional GSA method can easily fall into a trap of a local minim value to a certain degree, and accuracy of short period power prediction of a wind power field can be improved; defects that a conventional prediction method is not high in generalization capability, the complex conversion relation between the wind speed and the power cannot be demonstrated via a single model, and abrupt winds speed change is not taken into consideration in a prediction model and other defects can be overcome; accuracy of real time prediction of actual electricity generation power of the wind power field can be improved, and the short period wind electricity generation power prediction method has good application prospects.

Description

technical field [0001] The invention relates to the technical field of short-term wind power forecasting, in particular to a short-term wind power forecasting method based on a GSA-LSSVM model. Background technique [0002] With the rapid development of the world economy and society, traditional fossil energy is facing the threat of depletion, and renewable energy has been highly valued. With the increase of the total installed capacity of wind power at home and abroad, the characteristics of wind volatility and randomness determine the The instability of power generation and the low utilization rate of wind power faced by the wind power industry are becoming more and more obvious. Therefore, accurate prediction of real-time wind power is one of the important prerequisites for the stable and safe operation of the power grid. [0003] At present, short-term wind power forecasting methods can be divided into physical methods, statistical methods, artificial intelligence method...

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Problems solved by technology

[0006] The purpose of the present invention is to overcome the problem that in the prior art, there are few studies on the influence of wind speed fluctuation characteristics on wind power prediction, and the prediction accuracy cannot be improved.

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