Wind power prediction method

Abstract

The invention relates to a wind power prediction method. The method comprises the following steps: S1, decomposing an original wind power time sequence according to local mean decomposition; S2, constructing a respective training data set and a respective test data set for each PF component and margin; S3, establishing a wind power prediction model of an improved differential algorithm optimization extreme learning machine for each PF component and margin; S4, inputting the training samples of the plurality of PF components and the allowance in the training data set into a wind power prediction model of an improved differential algorithm optimization extreme learning machine one by one for training to obtain respective corresponding wind power prediction sub-models; S5, inputting the testdata into the corresponding wind power prediction sub-models for prediction; and S6, performing combination superposition processing on the prediction output value of each wind power prediction sub-model. According to the wind power prediction method, the influence of strong nonlinearity of the original wind power data on the prediction result is effectively reduced, and the wind power predictionresult with higher precision is obtained.

Description

technical field [0001] The invention relates to the technical field of wind power, in particular to a wind power prediction method. Background technique [0002] To meet growing global electricity demand, the use of renewable energy has increased significantly. Wind energy is an emerging renewable energy, and the total installed capacity has doubled in recent years. The instability of wind power is one of the obstacles to the combination of wind power system and the main power grid. In order to use the growing wind energy more safely and effectively, high-precision wind power prediction methods are of great significance to the operation of the power grid. [0003] The biggest challenge in forecasting wind power is its intermittency and uncertainty. The current forecasting methods can be divided into two categories based on physical models and based on historical data. Complex physical models always rely on numerical weather prediction (NWP) systems, but the required input...

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Problems solved by technology

[0004] In order to overcome the above-mentioned problems in the prior art that cannot achieve the optimal prediction effect, the problems of premature convergence of local optimum and large fluctuation of original wind power power in the optimization process, the present invention provides a wind power prediction method, which is A wind power prediction method based on local mean decomposition and improved difference algorithm to optimize the extreme learning machine, which effectively reduces the influence of the nonlinearity of the original wind power data on the prediction results, and avoids the non-optimal parameters of a single extreme learning machine , while solving the local optimum problem of the differential evolution algorithm

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