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Multi-step wind speed forecasting method based on Bayes robust function regression

A wind speed and function technology, applied in the field of new energy and statistical learning, can solve the problems of low precision and large error, achieve high precision, small error, and reduce the effect of abnormal points

Active Publication Date: 2018-09-21
TIANJIN UNIV +1
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Problems solved by technology

[0008] The purpose of the present invention is to solve the two major defects existing in the existing wind speed forecasting method, which leads to the technical problem of low accuracy and large error. The present invention provides a multi-step wind speed forecasting method based on Bayesian robust function regression

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  • Multi-step wind speed forecasting method based on Bayes robust function regression
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  • Multi-step wind speed forecasting method based on Bayes robust function regression

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specific Embodiment approach

[0050] Specific embodiments: this embodiment is a multi-step wind speed forecasting method based on Bayesian robust function regression, such as figure 1 As shown, the specific steps are as follows:

[0051] 1) Data preprocessing:

[0052] The 120 5-second wind speed points in every 10 minutes are regarded as a unit and stored in MATLAB, and then all the data in each unit are averaged to obtain a 10-minute average wind speed time series, and then the multi-step is determined according to the actual situation Predicted forecast step size, number of low-resolution forecast inputs and corresponding number of high-resolution wind speed inputs;

[0053] 2) Construct a multi-step wind speed forecast model with robust function regression:

[0054] Integrate the traditional regression model and the functional regression model to construct a functional regression model that can handle multi-resolution data The x, y represent the input and output of the model, the function represent...

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Abstract

The invention discloses a multi-step wind speed forecasting method based on Bayes robust function regression. The method comprises the following steps that: carrying out data preprocessing, constructing a multi-step wind speed forecasting model based on robust function regression, utilizing a variational Bayes optimization model parameter, and calculating a forecasting value according to an estimation parameter and a test set. By use of the multi-step wind speed forecasting method, various types of resolution data can be processed, a robustness effect can be performed for different practical forecasting tasks, in addition, an influence of a redundant function type variable on a final result can be lowered, accuracy is high, errors are small, and wind speed forecasting accuracy can be further improved.

Description

technical field [0001] The invention relates to the fields of new energy and statistical learning, in particular to a multi-step wind speed forecasting method based on Bayesian robust function regression. Background technique [0002] At present, wind power, as a clean and renewable energy source, has received more and more attention. Large-scale grid-connected wind power will alleviate the energy crisis to a certain extent, and can bring economic benefits and reduce environmental pollution. From the perspective of wind power grid connection, accurate wind speed and power forecast is an important factor to maintain the stability and safety of wind power system. [0003] At present, there are many wind speed and power forecasting methods. According to modeling theory, these methods can be roughly divided into five categories: physical models, traditional statistical models, artificial intelligence-based forecasting methods, spatial correlation models, and combined models. ...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 胡清华汪运王铮
Owner TIANJIN UNIV
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