摘要
为了解决风电功率神经网络预测输入变量多、计算效率低、泛化能力较差的缺点,采用主成分分析法(PCA)减少变量数。用神经网络动态集成的方法构建出较强泛化能力的BP网络集成。采用南方某风电场的数据进行了预测,比较了选取全部气象参数、部分气象参数和基于PCA处理后的数据作为神经网络输入对预测精度和计算效率的影响,结果表明采用PCA能在不降低预测精度的情况下,大大提高运算速度。通过对比单个和集成BP神经网络预测结果发现,采用集成网络的预测精度比单个BP网络精度有所提高,特别是风速突变的情况下更加明显。
The wind power artificial neural network （ANN） forecasting has shortcomings such as a large amount of variables, low computation efficiency and poor generalization ability. This paper proposes to apply the principal components analysis （PCA） to reduce the number of variables. Neural network dynamic integrating is adopted to establish the BP network integration with stronger generalization ability. Data of a wind power station in the South is used to forecast and compare the influence on accuracy and computation efficiency exerted by neural network input of all meteorological parameters, part of meteorological parameters and data based on PCA processing respectively. It is shown that using PCA processing can improve the computation efficiency greatly while keeping the forecast accuracy. Comparing the neural network forecasting results of single BP net with those of integrating BP net, we found that the latter one can perform better in improving the accuracy, especially in the case of sudden change of wind speed. This work is supported by National High-tech R ＆ D Program of China （863 Program） （No. 2011AA05A 105）.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2013年第4期50-54,共5页
Power System Protection and Control
基金
国家863计划项目(2011AA05A105)~~
关键词
风功率预测
主成分分析
神经网络集成
wind power forecast
principal components analysis （PCA）
ANN ensemble
