Wind power generation power interval prediction method, equipment and medium

Abstract

The embodiment of the invention provides a wind power generation power interval prediction method and device and a medium, and belongs to the technical field of measurement, and the method specifically comprises the steps: obtaining an initial data set and a test data set; extracting internal characteristics of the initial data set by using a variational mode decomposition method and a rolling fuzzy granulation method; inputting the training data set into the attention mechanism and the gating circulation unit neural network; training the initial prediction model based on the improved interval quality evaluation system to obtain an interval prediction model; judging whether the difference value of the evaluation indexes is smaller than a threshold value; if yes, inputting the test data set into an interval prediction model to obtain a wind power prediction interval combination; if not, continuing to train the initial prediction model until the difference value is smaller than the threshold value. According to the scheme of the invention, the internal features of the historical data are extracted, the upper and lower limits are further limited to form the training data set, the interval prediction model is trained, the corresponding wind power prediction interval is obtained, and the prediction efficiency, adaptability and prediction result accuracy are improved.

Description

technical field [0001] The embodiments of the present disclosure relate to the field of measurement technology, and in particular to a method, device and medium for wind power generation power interval prediction. Background technique [0002] At present, while the industry continues to develop, problems such as energy crisis and environmental pollution continue to threaten the sustainable development of human society. Renewable energy, as a feasible solution to alleviate the energy crisis, is getting more and more attention from all over the world. Due to its clean and non-polluting advantages, wind power is becoming more and more popular in modern power systems. However, compared to conventional energy sources, it is intermittent and uncertain due to the chaotic nature of weather and regional influences. As wind power penetration continues to increase, this may present some challenges for system operators. Accurate wind power forecasting is of great significance to the ...

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Problems solved by technology

[0005] In view of this, the embodiments of the present disclosure provide a wind power generation power interval prediction method, equipment and medium, which at least partially solve the problems of prediction efficiency and adaptability in the prior art and the problem of poor prediction accuracy

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