摘要
提出一种多风电场短期输出功率的联合概率密度预测方法。首先利用支持向量机对每座风电场的输出功率进行单点值预测,对预测误差建立稀疏贝叶斯学习模型进行误差的概率密度预测,得到单一风电场输出功率的边际概率密度函数预测结果;对多风场输出功率预测误差特性进行统计分析,发现同一区域内,风电场输出功率预测误差之间存在线性时空关联特性,进而运用动态条件相关回归模型求得相关系数矩阵,定量描述多风电场短期输出功率预测误差之间的动态时空相关关系;最后,综合单一风电场输出功率边际概率密度预测结果和相关系数矩阵得到多风电场输出功率的联合概率密度函数,并借助多元随机变量抽样技术形成包含动态时空关联特性的多维场景。通过实例分析,表明了所提出方法的有效性。
A method is proposed for forecasting joint probability density function （PDF） of short-term multiple wind farms”output power.Firstly,support vector machine is used to forecast single point value of the wind generation for each wind farm, and the PDF of prediction error is forecasted by sparse Bayesian learning;then the marginal PDF of wind generation is obtained.Secondly,the statistical property of prediction error of multiple wind farms”output power is analyzed to find the existence of temporal and spatial correlation properties.A dynamic conditional correlation regressive model is used to estimate the dynamic conditional correlation matrix,which can describe the quantitative relation of the temporal and spatial correlations. Finally,with the combination of PDF of each wind farm”s output power and conditional correlation matrix,the joint PDF of multiple wind farms output power can be formed, and it is further transformed into multi-dimensional scenarios using multivariate random variable sampling method.Test results illustrate the efficiency of the method.
出处
《电力系统自动化》
EI
CSCD
北大核心
2014年第19期8-15,共8页
Automation of Electric Power Systems
基金
国家重点基础研究发展计划(973计划)资助项目(2013CB228205)
国家自然科学基金资助项目(51007047
51077087)
山东省自然科学基金资助项目(ZR2010EQ035)~~
关键词
短期风电功率预测
联合概率密度预测
支持向量机
稀疏贝叶斯学习
动态条件相关回归模型
电力系统
short-term wind power forecast
joint probability density forecast
support vector machine
sparse Bayesian learning
dynamic conditional correlation regressive model
power systems
