Method and system for forecasting short-term wind speed of wind farm based on data driving

Abstract

The invention relates to a method for forecasting short-term wind speed of a wind farm based on data driving. The method comprises the following steps: S1, determining an input variable and an output variable of a relevance vector machine forecasting model according to a preset forecasting time interval; S2, training the relevance vector machine forecasting model by use of a training sample set; and S3, forecasting the wind speed according to the trained relevance vector machine forecasting model to obtain corresponding wind speed forecasting value. The invention also relates to a system for forecasting short-term wind speed of the wind farm based on the data driving. The system comprises a variable determination module for determining the input variable and output variable of the relevance vector machine forecasting model according to the preset forecasting time interval; a training model for training the relevance vector machine forecasting model by use of the training sample set; and a forecasting module for forecasting the wind speed according to the trained relevance vector machine forecasting model to obtain the corresponding wind speed forecasting value. The method provided by the invention is established based on the relevance vector machine, and can accurately forecast the wind speed. 本发明涉及一种基于数据驱动的风电场短期风速预测方法，包括步骤S1根据预设的预测时间间隔确定关联向量机预测模型的输入变量和输出变量；S2采用训练样本集对关联向量机预测模型进行训练；S3根据训练后的关联向量机预测模型进行风速预测，得到相应的风速预测值。本发明还涉及一种基于数据驱动的风电场短期风速预测系统，包括变量确定模块用于根据预设的预测时间间隔确定关联向量机预测模型的输入变量和输出变量；训练模块用于采用训练样本集对关联向量机预测模型进行训练；以及预测模块用于根据训练后的关联向量机预测模型进行风速预测，得到相应的风速预测值。本发明利用关联向量机建立，实现对风速精确估计。