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Neural network wind power short-term forecasting method based on fuzzy partition theory

A neural network and short-term forecasting technology, applied in biological neural network models, forecasting, data processing applications, etc., can solve problems such as lack of analysis, singleness of models, and low accuracy of wind power forecasting

Active Publication Date: 2015-06-24
国能日新科技股份有限公司
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Problems solved by technology

[0003] At present, wind power prediction mostly adopts the NWP power prediction model, without in-depth analysis of different wind speed levels and different time periods, and the model is single, and the accuracy of wind power prediction is low

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  • Neural network wind power short-term forecasting method based on fuzzy partition theory
  • Neural network wind power short-term forecasting method based on fuzzy partition theory
  • Neural network wind power short-term forecasting method based on fuzzy partition theory

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Embodiment Construction

[0054] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0055] The present invention will be described in detail below in conjunction with examples.

[0056] A neural network wind power short-term prediction method based on fuzzy partition theory, comprising the following steps,

[0057] Step 1), based on the fuzzy partition theory, the wind power level is divided into no wind (ZO), small wind (S), medium and small wind (MS), medium wind (M), medium and strong wind (MB), strong wind (B), super strong wind (BB) seven fuzzy partitions, also called fuzzy set A, and determine the membership function type of the fuzzy set A; said fuzzy set A means that for any x∈X, there is a definite number μ A (x)∈[0,1] corresponds to it, μ A (x) indicates the degree of membership of x relative to A, mapping:

[0058] mu A :X→[0,1]

[0059] (1.1)

[0060]...

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Abstract

The invention provides a neural network wind power short-term forecasting method based on a fuzzy partition theory. The neural network wind power short-term forecasting method based on the fuzzy partition theory adopts the mode of combining a fuzzy theory, artificial intelligence and a statistical theory through analyzing the important features of wind velocity variation and the relationship between wind velocity and power. When wind power forecasting is conducted, wind scale fuzzy partition processing is conducted on wind velocity data obtained from weather forecasting according to periods of time, BP neural network partition forecasting is conducted, a forecast power value is obtained through multiplying a partition forecast value by a membership degree value of the partition forecast value and adding all partition values, a probability statistics modified algorithm is conducted, and the forecast power is obtained. The neural network wind power short-term forecasting method based on the fuzzy partition theory improves the accuracy of a forecasting model effectively.

Description

technical field [0001] The invention belongs to the field of wind power prediction methods, in particular to a neural network short-term wind power prediction method based on fuzzy partition theory. Background technique [0002] The grid-connected capacity of wind power is increasing rapidly, and the connection between wind power and the system is getting closer and closer. It is necessary to consider the adverse effects of wind power output changes caused by wind power fluctuations and intermittences on power system power quality, safe and stable operation, and economic benefits. . Therefore, wind power forecasting has important practical significance. International wind speed and power prediction models are mainly physical models, statistical models, time series models, artificial intelligence models, etc. [0003] At present, wind power prediction mostly adopts the NWP power prediction model, without in-depth analysis of different wind speed levels and different time pe...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
Inventor 李丹丹向婕肖岐奎燕青浩
Owner 国能日新科技股份有限公司
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