Wind power short-term power prediction method based on relative error entropy evaluation method

Abstract

The invention discloses a wind power short-term power prediction method based on a relative error entropy evaluation method. The wind power short-term power prediction method comprises the following steps of: 1, acquiring historical data, and pre-treating the historical data to produce various training samples; 2, dynamically selecting the training samples, and establishing a bayesian neural network prediction model, an error feedback weighing time sequence prediction model and a wind power prediction unbiased grey verhulst prediction model; 3, respectively carrying out continuous prediction by adopting the three prediction models ten days ago from a prediction day; 4, respectively counting a relative error of each group of prediction data obtained in the step three, calculating an entropy and a variation degree coefficient of each group of relative error, and calculating a weight coefficient; 5, adopting the three prediction models to respectively carry out wind power prediction on the prediction day, and obtaining three groups of prediction data; and 6, carrying out combined prediction on the weight coefficient and the three groups of prediction data obtained in the step five to obtain a wind power short-term power prediction result. With the adoption of the wind power short-term power prediction method, the problem of determining the weight coefficient of combined prediction is solved, and the accuracy of wind power prediction can be improved.

Description

technical field [0001] The invention relates to a wind power short-term power prediction method, in particular to a wind power short-term power prediction method based on a relative error entropy value method. Background technique [0002] Wind energy is a renewable and clean energy source. Today's wind power generation mainly utilizes near-earth wind energy. The near-earth wind has the characteristics of volatility, intermittency, and low energy density, which leads to fluctuations in wind power. When large-scale wind farms are connected to the grid for operation, large wind power fluctuations will have adverse effects on the power balance and frequency regulation of the grid. Therefore, it is necessary to predict the power generation of wind farms. However, the wind power fluctuates greatly, and the accuracy of wind power prediction is low. [0003] At present, the combined forecasting method is an effective method to improve the accuracy of wind power forecasting. The ...

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Problems solved by technology

These two methods have been widely used, but still have the following defects: (1) The objective function constructed by the optimal combination method has a very complicated solution process, and the strong constraints of non-negative weights make the solution weights not optimal; (2) The basic idea of ​​combined forecasting i

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