电力系统自动化2012，Vol.36Issue(14)：125-130,142,7.
基于分量稀疏贝叶斯学习的风电场输出功率概率预测方法
Wind Farm Generation Forecast Based on Componential Sparse Bayesian Learning
摘要
Abstract
Probabilistic forecast is different from expectation forecast by the capability of forecasting the distribution of random variables. Based on the componential sparse Bayesian learning, this paper proposes a novel method to forecast the short-term wind farm generation. With this method, a time series of wind farm generation is decomposed into trend component and disturbance components by discrete wavelet decomposition Mallat algorithm. The trend component is then forecasted according to its strong correlation with wind speed and its self-correlation property,, while the disturbance components, which are more stationary, are forecasted according to their self-correlation property. A sparse Bayesian learning method is used to establish the forecasting model to give probabilistic forecasts to trend component, disturbance components, and as well as the total wind farm generation. Several learning machines are set up to fulfill a multi-step probabilistic forecast. Case study shows the effectiveness of the proposed method by continuous 7 200 times forecasting tests for a given actual wind farm.关键词
风电预测/概率预测/稀疏贝叶斯学习/离散小波变换/电力系统Key words
wind power forecast/probabilistic forecast/sparse Bayesian learning/discrete wavelet transform/power system分类
交通工程引用本文复制引用
杨明,范澍,韩学山,LEE Wei-Jen..基于分量稀疏贝叶斯学习的风电场输出功率概率预测方法[J].电力系统自动化,2012,36(14):125-130,142,7.基金项目
国家自然科学基金资助项目 （）
国家高技术研究发展计划（863计划）资助项目 （）
高等学校博士学科点专项科研基金资助项目 （）
山东省自然科学基金资助项目（ZR2010EQ035）~~ （）
