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Wind power output short-term interval prediction method

A technology of wind power output and forecasting method, which is applied in the information field to achieve the effect of ensuring smoothness and precision, improving accuracy, and reliable support

Active Publication Date: 2020-06-30
DALIAN UNIV OF TECH
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Problems solved by technology

[0004] The invention mainly solves the problem of short-term interval prediction of wind power output

Method used

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  • Wind power output short-term interval prediction method
  • Wind power output short-term interval prediction method
  • Wind power output short-term interval prediction method

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Embodiment Construction

[0022] Wind power development started mainly in the "Three Norths" and other places. In recent years, the scale of wind power development in the central, eastern, and southern regions has gradually increased. The construction of wind farms needs to consider regional power development planning, environmental constraints, meteorological factors, wind energy resources, and terrain. conditions and other factors. At present, wind energy resources are still the primary condition for site selection of domestic wind farms. Some areas have abundant wind energy resources, but the wind power output in these areas is far greater than the load demand, resulting in waste of resources, and the volatility of wind energy is easy to cause impact on the power grid. , wind power output interval prediction can not only be used for power resource scheduling and reduce waste of resources, but also can predict fluctuation information in advance, so as to weaken its volatility and reduce damage to the ...

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Abstract

The invention belongs to the technical field of information, and provides a wind power output short-term interval prediction method. According to the invention, the method comprises the steps: employing industrial real data, constructing a multi-level information granularity non-equal-length distribution structure, and establishing a corresponding optimization model; furthermore, considering the importance of the model structure to the prediction precision, and carrying out reinforcement learning on the structure parameters of the multi-level model through the Monte Carlo method; and finally,based on the optimal multi-layer granularity calculation structure, acquiring a long-term interval prediction result of the coal gas production and dissipation amount by applying a parallel calculation strategy. The result obtained through the method is high in precision, the calculation efficiency meets the actual application requirement, and the method can also be applied and popularized in other energy medium systems in the iron and steel industry.

Description

technical field [0001] The invention belongs to the field of information technology, relates to signal decomposition, neural network modeling, non-parametric estimation and other technologies, and is a short-term interval prediction method of wind power output combined with deep learning and non-parametric estimation. The present invention uses industrial actual operation data, and first proposes an improved self-adaptive variational mode decomposition method to decompose the wind speed signal, so as to reduce the non-stationarity of the data samples. Furthermore, the sample entropy method is used to divide the decomposed signal into high-frequency part and low-frequency part, and the deep belief network is used to predict respectively, and the predicted results of each component are reconstructed to obtain the predicted value of the wind speed signal, and the wind power output is predicted based on the BP neural network. Point base value. Finally, an improved non-parametric ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/084G06N3/044Y04S10/50
Inventor 赵珺金锋高兴兴王霖青王伟
Owner DALIAN UNIV OF TECH
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