Wind power combination probability prediction method considering evaluation index conflicts

Abstract

The invention discloses a wind power combination probability prediction method considering evaluation index conflicts. The method is characterized by comprising the steps of determining a decomposition parameter K through variational mode decomposition optimized on the basis of the law of conservation of energy, decomposing an original wind power signal into a series of intrinsic mode function components, removing an intrinsic mode function with the minimum amplitude, and combining the remaining intrinsic mode functions to obtain a wind power sequence after fluctuation and randomness are reduced; constructing an input feature set containing 96-dimensional historical features by using the wind power sequence, and constructing different GPR models by using 10 covariance functions; calculating the area grey correlation closeness based on the five indexes by adopting an area grey correlation decision-making method so as to comprehensively evaluate the performance of each prediction model and solve the conflict between evaluation indexes; and calculating the weights of different GPR probability prediction models in the combined model according to the area grey correlation closeness, constructing the combined model, and carrying out wind power probability combined prediction by using the combined probability prediction model.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting in electric power systems, and relates to a wind power combination probability forecasting method taking into account evaluation index conflicts. Background technique [0002] The purpose of wind power forecasting is to provide an important basis for grid operation. Most of the existing wind power prediction methods are point predictions, and the results generally have different degrees of error, which cannot describe the probability distribution of potential wind power. Compared with deterministic prediction, the prediction results obtained by probabilistic prediction method contain more information, which can quantify the uncertainty of wind power. High-quality probabilistic prediction results are helpful for wind power risk analysis and assessment, and reduce the negative impact of wind power uncertainty. [0003] The current probabilistic prediction of wind power often uses a ...

AI Technical Summary

Problems solved by technology

However, different indicators have different emphases. When using different indicators to evaluate probabilistic wind power forecasting models, there may be conflicts between the conclusions of the indicators, that is, the optimal models determined by different deterministic or probabilistic indicators are different.

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