Short-term wind power prediction method, device and system

Abstract

Embodiments of the invention disclose a short-term wind power prediction method, device and system. the method comprises the following steps of: obtaining wind power history data, and preprocessing the wind power history data to obtain training sample data and test sample data; predicting the test sample data by adoption of a pre-established extreme learning machine optimization model so as to obtain a wind power prediction result, wherein the extreme learning machine optimization model is established through adding the training sample data into an extreme learning machine; and optimizing parameters of the extreme learning machine by adoption of a chaotic crisscross algorithm-combined particle swarm algorithm so as to obtain a trained extreme learning machine optimization model, wherein the parameters comprise an input weight and hidden layer offset. According to the method, device and system, the local search ability and global convergence precision of the extreme learning machine optimization model are improved, and the optimized extreme learning machine optimization model is adopted to predict the test sample data, so that the obtained prediction result is more accurate.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of wind power generation, in particular to a short-term wind power prediction method, device and system. Background technique [0002] With the continuous growth of global power demand, renewable energy has been widely used. Wind energy, as an emerging renewable energy, has promoted the development of wind power technology. However, the instability of wind power is one of the obstacles to the combination of wind power system and the main power grid. In order to use the growing wind energy more safely and effectively, high-precision wind power prediction methods are of great significance to the operation of the power grid. [0003] At present, the existing technology mainly uses a single model to predict short-term wind power, such as time series method, gray model method, artificial neural network, support vector machine and extreme learning machine, etc. However, due to the nonline...

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Problems solved by technology

However, the instability of wind power is one of the obstacles to the combination of wind power system and the main power grid. In order to use the growing wind energy more safely and effectively, high-precision wind power prediction methods are of great significance to the operation of the power grid.

[0003] At present, the existing technology mainly uses a single model to predict short-term wind power, such as time series method, gray model method, artificial neural network, support vector machine and extreme learning machine, etc. However, due to the nonlinearity of wind power time series Strong and non-stationary characteristics, so it is difficult to accurately predict short-term wind power with a single model

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