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

A firefly algorithm and Bayesian network technology, applied in the field of wind power generation, can solve problems such as optimal solution, slow convergence speed, and point prediction error cannot be eliminated

CN110264006AActive Publication Date: 2019-09-20HEFEI UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2019-09-20

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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.
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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...

Examples

Embodiment Construction

[0071] In this embodiment, a wind power probabilistic prediction method based on chaotic firefly algorithm and Bayesian network, such as figure 1 As shown, including: obtaining wind speed, wind direction, air temperature and wind power actual power data, and preprocessing the data; performing EMD decomposition on wind power actual power to reduce the volatility of wind power; establishing a Bayesian network model to obtain the initial prediction interval ;Calculate the amplitude range of the interval change, and use the chaotic firefly algorithm to obtain the optimal interval change range when the fitness function is optimal, so as to obtain the final prediction interval, and analyze and evaluate the prediction results. Specifically, proceed as follows:

[0072] Step 1. Obtain wind speed, wind direction, air temperature and actual wind power data and perform data preprocessing:

[0073] Step 1.1, collect the historical data of wind speed to form the original wind speed sequen...