Short-term wind speed forecasting method based on local integrated study

An integrated learning and wind speed technology, applied in forecasting, data processing applications, calculations, etc., can solve problems such as overfitting, forecast results that cannot meet actual requirements, and poor generalization ability of algorithms

Inactive Publication Date: 2015-07-15
TIANJIN UNIV
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Problems solved by technology

However, local learning has a strong learning ability, which will cause over-fitting problems, making the generalization ability of the al

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  • Short-term wind speed forecasting method based on local integrated study
  • Short-term wind speed forecasting method based on local integrated study
  • Short-term wind speed forecasting method based on local integrated study

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Embodiment Construction

[0038] The wind speed forecasting method based on local integrated learning relates to the wind speed forecasting method. There are differences in wind speed at different times and the changes in wind speed are complex and diverse. Using a global forecast method to obtain a single complex training model cannot accurately describe the complex relationship between wind speeds. Local learning is a new technique for solving complex problems. Due to the strong learning ability of local learning, there will be over-fitting problems. We integrate ensemble learning into local learning and propose a local ensemble learning algorithm. For a given sample, first use the K nearest neighbor method to find the K closest samples as the training set of the current sample point, and then send this training set to several independent base learners and learn the corresponding prediction results. A certain fusion strategy can obtain the final prediction result of the current sample point and impr...

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Abstract

The invention discloses a short-term wind speed forecasting method based on local integrated study. The method comprises steps as follows: step one, K wind speed samples most similar to a measured wind speed sample point are found out with a K-nearest neighbor algorithm; step two, a corresponding wind speed predicted value of a local sample of each wind speed sample point is obtained; step three, a plurality of wind speed predicted values obtained in the step two are fused by adopting certain fusion strategies to obtain a predicted value of a current wind speed point. The short-term wind speed forecasting method based on the local integrated study is proposed firstly, and the complexity and the diversity of wind speed sample change are considered; mathematical theory knowledge of the local integrated study algorithm is provided; difference of different wind speed samples is effectively obtained, different wind speed forecasting models are established according to different wind speed sample points, and compared with an overall wind speed forecasting model, a local wind speed forecasting model and a whole integration wind speed forecasting model, the forecasting error rate is reduced by more than 10%.

Description

technical field [0001] The invention belongs to the technical field of wind power generation, in particular to a short-term wind speed forecast method based on local learning. Background technique [0002] Due to the continuous increase of energy consumption and the increasingly severe environmental pollution, many countries have carried out large-scale exploration of new energy sources. Among them, wind energy, as an important renewable resource, has attracted more and more attention from all over the world. With the maturity of wind power generation technology, wind power supply and demand must be balanced when wind power is connected to the grid. However, wind power has strong fluctuations and uncertainties, which have a major impact on the dispatching method, stability and peak frequency regulation of the power grid. Accurate forecasting of wind power is helpful for power grid dispatching departments to timely grasp the output power of wind farms and adjust dispatching ...

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Application Information

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IPC IPC(8): G06Q10/04
Inventor 胡清华于曼
Owner TIANJIN UNIV
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