Wind power probability prediction method based on hierarchical integration

Abstract

The invention discloses a wind power probability prediction method based on hierarchical integration. According to the method, a subspace set is constructed through resampling and a partial least square method, a plurality of local areas are obtained on each subspace through GMM clustering, a corresponding local GPR model is established, and a Bayesian reasoning strategy and a finite mixing mechanism are used for fusing the local models to establish a first-layer integrated model. And a genetic algorithm is adopted to select a suitable first-layer integration model for selective adaptive integration, so that a selective hierarchical integration Gaussian process regression probability prediction model can be obtained. In order to solve the problem of performance deterioration caused by change of wind power data characteristics, an adaptive updating strategy is introduced, so that the prediction model has adaptive updating capability. According to the method, the selective hierarchical ensemble learning framework is used for ultra-short-term wind power prediction, compared with a traditional ensemble learning prediction method, the method has higher prediction precision and stability, and the generated prediction interval can provide effective reference for power dispatching.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, in particular to a probabilistic wind power forecasting method based on hierarchical integration. Background technique [0002] Wind energy is a non-polluting, widely distributed renewable energy, and wind power generation technology has developed rapidly in recent years. However, due to the randomness and volatility of wind energy, unstable wind power access to the grid will have an impact on the safety and stability of the power system, thereby affecting the stable operation of grid equipment. Therefore, accurate and efficient wind power forecasting can effectively promote the arrangement of reasonable power dispatching, provide a reliable reference for the power grid to arrange power generation plans, shutdown maintenance, and help ensure the safe, reliable and economical operation of the system. Wind power prediction plays a vital role in the development of the power generatio...

AI Technical Summary

Problems solved by technology

However, the problem of adaptation of ensemble models has been little explored in recent studies

[0004] Finally, due to the strong randomness and high uncertainty of wind energy, the traditional single-point prediction cannot make a good estimate of the wind uncertainty. For the stability of the power system, the integration of wind power needs to There is a relatively accurate estimate of the fluctuation range of the market, and a single-point forecast is not enough

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