Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68721
Title: Method and system for forecasting short-term wind speed of wind farm based on hybrid neural network
Other Titles: 基于混合神经网络的风电场短期风速预测方法和系统
Authors: Dong, CY 
Huang, JB 
Meng, K 
Issue Date: 30-May-2012
Publisher: 中华人民共和国国家知识产权局
Source: 中国专利 ZL 201010557446.6 How to cite?
Abstract: The invention relates to a method for forecasting short-term wind speed of a wind farm based on hybrid neural network. The method comprises the following steps: S1, determining an input variable and an output variable of a hybrid neutral network forecasting model according to a preset forecasting time interval; and S2, forecasting the wind speed according to the hybrid neutral network 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 hybrid neural network. The system comprises a variable determination module for determining the input variable and output variable of the hybrid neutral network forecasting model according to the preset forecasting time interval; and a forecasting module for forecasting the wind speed according to the hybrid neutral network forecasting model to obtain the corresponding wind speed forecasting value. The method and the system provided by the invention have advantages of high computation speed and high reliability, solve the technical problem completely depending on a physical forecasting model and overcome the disadvantage of large forecasting error fluctuation based on a single model.
本发明涉及一种基于混合神经网络的风电场短期风速预测方法,其中包括步骤S1、根据预设的预测时间间隔确定混合神经网络预测模型的输入变量和输出变量;S2、根据所述混合神经网络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混合神经网络的风电场短期风速预测系统,其中包括变量确定模块:用于根据预设的预测时间间隔确定混合神经网络预测模型的输入变量和输出变量;以及预测模块:用于根据所述混合神经网络预测模型进行风速预测,得到相应的风速预测值。本发明的基于混合神经网络的风电场短期风速预测方法和系统计算速度快、可靠性高、解决了完全依赖物理预测模型的技术难题、又可以克服单一模型预测误差波动大的缺陷。
URI: http://hdl.handle.net/10397/68721
Rights: 专利权人: The Hong Kong Polytechnic University.
Appears in Collections:Patent

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