Data analysis and combination primary function nerve network-based wind power prediction method

Abstract

A data analysis and combination primary function nerve network-based wind power prediction method is disclosed and relates to the field of wind power prediction. The method comprises the following steps: collected wind power data is subjected to data analyzing operation via variable mode decomposition operation, sample entropy technologies and phase space reconstruction technologies; four groups of subsequences are obtained; primary function nerve networks are built via orthogonal polynomials, and a combined primary function prediction model containing four groups of primary function nerve networks is built; a state transition algorithm is used for optimizing weight and threshold values of the primary function nerve networks, reconstructed subsequences are used as input for the primary function nerve networks, and the optimized prediction model combination primary function nerve networks are used for predicting wind power. Prediction accuracy of the method is markedly higher than that of a BP network and that of an RBF nerve network.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, and belongs to a wind power forecasting method based on data processing and a combined basis function neural network. Background technique [0002] The World Wind Energy Association announced at the end of 2016 that the world's total installed capacity reached 435 GW, with an overall growth rate of 17.2%. In the past ten years, wind power has become one of the fastest growing clean energy sources. However, the indirectness and randomness of wind force have a great impact on the stability of wind power generation system, which has become a huge challenge to the dispatching operation and safety and stability of the power system. Effective wind power forecasting can reduce power system reserve capacity, reduce system operating costs, and improve system economy and reliability. [0003] Wind power forecasting models mainly include physical forecasting models and statistical forecasti...

AI Technical Summary

Problems solved by technology

Therefore, a single forecasting method has its own limitations in forecasting and cannot guarantee stable and excellent forecasting accuracy for any data sample. Therefore, the establishment and application of a comprehensive forecasting model has received more and more attention

At present, most of the existing wind power prediction methods have shortcomings such as short prediction lead time, low prediction accuracy, and long prediction time.

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