Wind power probability prediction method based on chaotic firefly algorithm and Bayesian network

Abstract

The invention discloses a wind power probability prediction method based on a chaotic firefly algorithm and a Bayesian network, and the method comprises the steps of 1, obtaining the wind speed, the wind direction, the air temperature and the wind power actual power data, preprocessing the data, and selecting the data of a training set and a test set; 2, performing empirical mode decomposition on the original data of the wind power to enable a wind power time sequence to be more stable; 3, constructing a Bayesian network model to obtain an initial prediction interval; 4, calculating an interval change amplitude range, and obtaining an optimal interval change amplitude by using a chaotic firefly algorithm; and 5, performing chaotic search near the optimal interval change amplitude to obtain a final prediction interval. According to the method, the uncertainty of measuring the wind power by constructing the prediction interval can be constructed, so that an effective reference can be provided for a power dispatching decision.

Description

technical field [0001] The invention relates to the technical field of wind power generation, and mainly relates to a wind power prediction model method based on EMD decomposition, Bayesian network and chaotic firefly algorithm. Background technique [0002] In recent years, with the development of society and economy, the demand for energy is increasing day by day. Fossil energy is gradually facing the risk of depletion. At the same time, the use of a large number of fossil fuels has brought serious pollution problems. In order to deal with the problems of fuel energy and environmental pollution, renewable energy has been widely used. Among them, wind energy, as a clean, renewable, and most potential energy source, has been rapidly developed and applied in the past few decades. With the gradual expansion of the scale of wind power generation, wind power occupies an increasing proportion in the power system. However, the weak controllability of wind power caused by the int...

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Problems solved by technology

However, due to the uncertainty of wind energy itself and the inherent defects of the model, the traditional deterministic point prediction method has problems such as point prediction errors that cannot be eliminated, uncertainty in the results cannot be measured, and the fluctuation range of wind power cannot be given.

[0005] In the current probabilistic prediction methods of wind power, evolutionary algorithms are mostly used to optimize the model. However, although the traditional evolutionary algorithms have a strong global search ability and can effectively improve the convergence speed of the model, they generally have a slow convergence speed in the later stage. The model is prone to prematurity, and it is easy to fall into the defect of local optimal solution

In wind power forecasting, the algorithm will run for a long time and fail to meet the time availability requirements; the prediction accuracy is not high and cannot meet the accuracy and availability requirements

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