Ultra-short-term wind power prediction method according to off-line track characteristic optimization and real-time extrapolation model matching

Abstract

The invention discloses an ultra-short-term wind power prediction method according to offline track characteristic optimization and real-time extrapolation model matching, and belongs to the fields of development and utilization of renewable energy sources. The method comprises the following two steps: (1) establishing a model in an off-line mode and optimizing parameters, namely dividing a track formed by historical data of a wind power time sequence or a wind speed time sequence into different forms according to the given characteristic quantity, respectively establishing a prediction model for each form, and optimizing the parameters; (2) performing real-time prediction, namely calling a corresponding prediction model according to the forms of the track of latest measured data. According to the method, the time-varying characteristics of the wind power sequence are fully measured, and the statistical characteristics and change rules of a wind power sequence at different time intervals are reflected. The defect that dynamic change of the wind power sequence and statistical characteristics of the wind power sequence at the different time intervals can not be comprehensively reflected in a traditional wind power prediction method is overcome. The coordinative optimization among prediction models (or algorithms) is realized. Therefore, the prediction accuracy is improved, and the prediction efficiency is also improved.

Description

technical field [0001] The invention belongs to the field of renewable energy development and utilization. More precisely, the invention relates to an ultra-short-term prediction method of wind power or wind speed. Background technique [0002] Vigorously developing clean energy technologies such as wind power plays a very positive role in improving the energy structure and reducing greenhouse gas emissions. However, compared with conventional power sources, the output power of wind farms has the characteristics of volatility, intermittent and randomness. The centralized grid connection of a large number of wind farms will affect the safety, stability, and economic operation of the grid, and become a limit for grid acceptance. One of the major hurdles for wind power. [0003] Predicting the output power of wind farms is one of the most effective and economical means to improve the peak-shaving capability of the power grid, enhance the ability of the power grid to accept win...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings of traditional wind power forecasting methods that cannot fully reflect the dynamic changes of wind power sequences or wind speed sequences and the statistical characteristics of different time periods, and provide a method that can fully take into account the time-varying nature of wind power sequences and reflect wind power in different time periods. Ultra-short-term wind power or wind speed prediction method based on the statistical characteristics and changes of the sequence

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