Short-term wind power generation prediction method, device, equipment and storage medium

Abstract

The invention discloses a short-term wind power generation prediction method and device, equipment and a storage medium, and relates to the field of new energy power generation prediction, and the method comprises the following steps: obtaining main meteorological characteristics affecting the generated power, and combining the historical data of the main meteorological characteristics with the historical data of the generated power to obtain a target data set; setting and initializing parameters of the multivariate universe algorithm and the kernel extreme learning machine; dividing the target data set into a training set and a test set, and training a kernel extreme learning machine by using the training set; running a multivariate universe algorithm to obtain an optimal combination of penalty parameters and kernel parameters in the kernel extreme learning machine; and substituting the optimal combination into a kernel extreme learning machine, and testing the test set by using the kernel extreme learning machine with the optimal combination to obtain a prediction result. According to the scheme, the parameters of the kernel extreme learning machine are optimized by using the multivariate universe algorithm, the problems of local optimal solution and the like can be avoided, and the prediction precision of the model is improved.

Description

technical field [0001] The present application relates to the field of new energy power generation prediction, and in particular to a short-term wind power generation prediction method, device, equipment and storage medium. Background technique [0002] The development of modernization is mainly driven by electrification and informatization, which requires a reliable and stable power supply as a support. For this reason, renewable energy power supply technology for modernization has emerged to achieve clean, efficient, reliable and economical power supply. Renewable energy power supply technology has the characteristics of clean and environmental protection, and is an inevitable choice for modern green energy use. It does not consume fossil energy once in use, has no carbon emissions, and does not produce greenhouse effects. At the same time, renewable energy power supply facilities are compact in structure and less Occupy or not occupy arable land, easy to replace, easy to ...

AI Technical Summary

Problems solved by technology

With the integration of large-scale wind power into the grid, how to ensure that the power system can maximize the benefits while reducing the impact of wind power generation, researchers in related fields have proposed many methods, most of which focus on the development of forecasting tools to predict Wind power generation power. In recent years, more and more researchers have combined some related computer technologies with wind power generation prediction work to achieve better prediction of power generation. However, the prediction effects of these technologies are uneven, so how to Efficient and accurate forecasting of power generation has become an urgent problem to be solved

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