Multi-model combined prediction method for short-term power of wind farm

Abstract

A multi-model combined prediction method for the short-term power of a wind farm is used for improving the prediction precision of the short-term power of the wind farm.According to the technical scheme, the method comprises the steps that single-step and multi-step prediction of variable-weight dynamic combination are conducted on the wind speed by adopting multiple prediction models according to actual data collected in the wind farm, wherein multi-step prediction is implemented by adopting a rolling prediction method and equivalent dimensions addition of data, and the weight proportion among the models is regulated by analyzing the prediction precision of the models in real time; a prediction result of the output power of the wind farm is obtained according to a wind speed power curve fitted by measured data.According to the prediction method, the weight proportion among the models is changed by analyzing the prediction precision of the models on the basis of a data-driven idea, not only is comprehensive utilization of data information of different prediction methods achieved, but also the condition that the real-time prediction precision is influenced by the fixed weight coefficient can be prevented, therefore, the wind speed prediction precision is improved, and the prediction effect on the short-term power of the wind farm is guaranteed.

Description

technical field [0001] The invention relates to a method capable of accurately predicting the short-term output power of a wind farm, which belongs to the technical field of power generation. Background technique [0002] In recent years, with the rapid development of human society, problems such as energy crisis and environmental pollution have gradually become prominent, and the development and utilization of clean energy have been widely valued and vigorously developed by countries all over the world. Wind energy, as a renewable energy, has the advantages of cleanliness, abundant reserves, and convenient development and utilization, making the wind power industry develop rapidly. [0003] China's wind power is in a stage of rapid growth, especially after the large-scale smog in Beijing and northern China, this development trend will continue, and the control of air pollution has become another new driving factor for the development of wind power. [0004] As of 2013, the...

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Problems solved by technology

[0008] Although there are various forecasting methods for wind power forecasting, the forecasting accuracy of the existing forecasting methods is not ideal, and further research is necessary

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