Method for determining self-adaption dynamic weight of combined prediction model for wind electricity power

Abstract

The invention discloses a method for determining self-adaption dynamic weight of a combined prediction model for wind electricity power. The method comprises the steps of firstly according to the homogeneity characteristic of probability and a weight coefficient, determining the weights of each sub-model in a combined model by means of combining an improved probability weighting approach with an optimization approach so as to obtain an optimal weight coefficient distribution approach in a static combined model; on the basis of the static optimal weight coefficient distribution approach, carrying out self learning to obtain an optimal weight coefficient distribution approach in a dynamic combined model as well as a self-adaption combined model; dynamically calculating the optimal weight of the combined model by a sliding translation model. Since the dynamic optimal weight coefficient distribution approach achieves self-adaption tracking regulation of wind electricity power prediction and does not have strict requirements on the probability distribution of wind electricity power data, the method for determining the self-adaption dynamic weight has a wide adaption range and a high engineering practical value, and the instability and errors of combined model predication of wind electricity power can be effectively reduced.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, and in particular relates to a dynamic self-adaptive determination method for the weight of a wind power combination prediction model. Background technique [0002] The fluctuation and instability of wind lead to the instability of wind power, so that the ability of the grid to utilize wind power is not strong. Therefore, accurate and stable wind power prediction is a hot topic in the research of wind power technology. [0003] The combined forecasting method is a forecasting method proposed by Bates and Granger in 1969. Its basic idea is to combine different forecasting methods and models through weighting, make full use of the information provided by each model, comprehensively process the data, and finally obtain a combined forecast result. At present, the methods for determining the weight coefficients of many models are: equal weight average method, minimum variance method, u...

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Problems solved by technology

Compared with a single prediction model, the combined model of weight coefficient determined by equal weight average method, minimum variance method, and unconstrained least square method can improve wind power prediction to a certain extent, but these methods cannot effectively use different wind power when determining weight coefficients. Modeling based on the difference of wind power data in the field can not reflect the physical difference of different electric fields, and also keeps the weight coefficient of the sub-model in the combined model unchanged

This combination model with weight coefficients kept constant has certain limitations, so the prediction accuracy for different wind farm combination models is not high

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