Multi-time step wind power prediction method based on dynamic feature selection

Abstract

The invention discloses a multi-time step wind power prediction method based on dynamic feature selection. According to the method, an intelligent hybrid model method for mining a historical power time sequence and disclosed numerical weather prediction (NWP) data by utilizing a dynamic feature extraction algorithm is provided, so that the challenge that wind power generation is difficult to predict under different time steps is solved. According to the method, on the basis of available original data, a dynamic filtering method with minimum redundancy and maximum correlation (mRMR) is adoptedto automatically select input variables with different prediction step lengths; secondly, supervised learning is conducted on the input data with the optimal characteristics through an adaptive neuralfuzzy inference system (ANFIS); ANFIS parameters are trained by the model by using a particle swarm optimization (PSO) algorithm so as to achieve an optimal prediction effect; and finally, the proposed hybrid intelligent model is evaluated through the operation data of the actual distributed wind turbine generator, and the effectiveness of the model is verified through experimental results.

Description

technical field [0001] The invention relates to the field of multi-time-step wind power prediction for new energy power generation, in particular to a multi-time-step wind power prediction method based on dynamic feature selection. Background technique [0002] In recent years, the urgent need for a low-carbon economy and the advancement of wind power technology are driving the rapid and sustainable transformation of the energy industry and the development of global wind power. Due to the difficulty of grid-connected wind power generated by intermittency and randomness, the use of active distribution network technology is considered to be an effective technical approach for large-scale utilization of renewable wind power energy, and one of the key technologies is the ability to "actively" distribute wind power energy. performance analysis and prediction. Accurate multi-time-step wind power forecasting can improve wind power utilization and system reliability, reduce operati...

AI Technical Summary

Problems solved by technology

However, direct analysis and prediction of available information cannot fully explore the relationship between future wind power output and various factors, resulting in information redundancy and noise submersion of important models, making multi-time step wind power prediction a multi-variable and highly nonlinear complex problem

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