Wind energy prediction method which combines spatial-temporal features and error processing

Abstract

The invention discloses a wind energy prediction method which combines spatial-temporal features and error processing. The method comprises the following steps: extracting time features from a wind energy time sequence; extracting the information between the wind motors close to each other via a multi-input and single-output mode and introducing spatial features; preprocessing the spatial-temporalfeatures by a k-nearest neighbor based noise data detection method; analyzing the variance attributes of the preprocessed spatial-temporal features, and training multiple predictor models based on the result of the analysis; integrating the multiple predictor models by a weighted average method to generate an integrated learning model based on the spatial-temporal feature variance, which is usedto forecast the error of the integrated predictor model; using the integrated learning model to get a predicted value y, inputting the error features corresponding to the spatial-temporal features into an auxiliary model to get the result y ', then getting the final predicted value y + y'; and combining the integrated learning model with the auxiliary model to generate the final model.

Description

technical field [0001] The invention relates to the fields of data mining, feature engineering and wind energy forecasting, in particular to a wind energy forecasting method combining spatiotemporal features and error processing. Background technique [0002] Currently, machine learning algorithms for wind energy forecasting mainly include artificial neural networks, decision trees, and support vector machine regression. Since the problem of wind energy prediction itself is a problem of "predicting values ​​based on features", it has good compatibility with general machine learning methods, so that most of the commonly used machine learning methods include: random forest, neural network, and various regression Algorithms, etc. can be easily migrated and applied to this field. [0003] At present, it is impossible to prove that one model is better than another model: first, there is no generally accepted evaluation standard to judge each model, and second, to compare the eff...

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a wind energy prediction method that combines spatio-temporal features and error processing. The present invention can extract more effective features combined with integrated learning, effectively overcoming the traditional single model that can only be applied to specific scenarios in prediction and does not have universal applicability. For technical issues, please refer to the description below:

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