Wind power prediction method and system integrating long-term and short-term memory network and extreme learning machine

Abstract

The invention provides a wind power prediction method integrating a long-term and short-term memory network and an extreme learning machine, and the method comprises the steps of obtaining a wind power sequence and corresponding meteorological data, and carrying out the recombination of the feature data and meteorological feature data of the wind power sequence according to the frequency size, andforming a low-frequency combined input feature vector and a high-frequency combined input feature vector; inputting the low-frequency combined input feature vector into a trained long-term and short-term memory network prediction model to obtain a first prediction result, and inputting the high-frequency combined input feature vector into a trained extreme learning machine prediction model to obtain a second prediction result; and fusing prediction results of the long-term and short-term memory network prediction model and the extreme learning machine prediction model to obtain a final prediction result of the wind power. Different prediction models are set for components of different frequencies, and the prediction result of the prediction model is fused so that the wind power predictioneffect can be improved. Meanwhile, the strong coupling effect of the wind power meteorological information and the wind power is fully considered, and the accuracy of wind power prediction is improved.

Description

technical field [0001] The present disclosure relates to the technical field related to wind power generation, and specifically relates to a wind power forecasting method and system that integrates a long-term short-term memory network and an extreme learning machine. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Wind power is a clean renewable energy. In recent years, the rapid development of wind power has greatly alleviated the shortage of energy in our country. However, the volatility and randomness of wind power significantly increase the uncertainties of the power system, which has a negative impact on the smooth operation and real-time dispatch of the power system. Moreover, with the rapid development of wind power integration, the proportion of wind power in the total electricity consumption of the society is constantly incre...

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Problems solved by technology

[0006] In order to solve the above problems, this disclosure proposes a wind power forecasting method and system that integrates long-term and short-term memory networks and extreme learning machines, fully considers the strong coupling effect of wind power meteorological information and wind power power, and fully considers the frequency characteristics of wind power sequences. The data is processed by different models, which solves the problem of poor prediction effect of a single model

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