Wind power combined prediction method considering spatial correlation and corrected numerical weather forecast

Abstract

The invention discloses a wind power combined prediction method considering spatial correlation and corrected numerical weather forecast, aiming at the defects of the traditional GP based on a singlekernel function, combining a plurality of kernel functions to obtain an optimal kernel function scheme, and establishing a prediction model on the basis of the GP based on the combined kernel function. In consideration of high data complexity of multi-dimensional meteorological factors, key factors are screened through an automatic correlation judgment algorithm, and an NWP wind speed deviation correction model is established. And meanwhile, wind speed time sequences of the target wind power plant and the adjacent wind power plant are analyzed by utilizing a spatial correlation method, a timedifference corresponding to the strongest correlation is solved by utilizing a Pearson correlation coefficient method, and a spatial correlation model is established. And based on a combined weightedprediction model of the above model, a combined model weight coefficient is obtained through a Lagrange method. Therefore, the numerical weather forecast deviation correction method and the spatial correlation method are effectively combined through the combined weighted prediction model, and the prediction precision of the wind power is improved.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, in particular to a wind power combination forecasting method that takes into account spatial correlation and corrected numerical weather forecast. Background technique [0002] The depletion of fossil energy and the environmental pollution it brings are becoming more and more serious, and the task of developing renewable energy is imminent. Wind energy, as a green and non-polluting renewable energy, has seen a continuous increase in the penetration rate in the power grid in recent years. According to the data provided by the Global Wind Energy Council, by 2018, the global installed capacity of wind power has reached 591GW, of which China has 184GW, ranking first in the world in terms of wind power installed capacity. However, the randomness and intermittency of wind power output have caused great difficulties in its accurate prediction, and brought certain challenges to the safe a...

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Problems solved by technology

At the same time, none of the existing studies have taken into account factors such as the temporality and spatiality of wind power prediction and the inaccuracy of numerical weather prediction.

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