摘要
针对光伏发电系统的输出功率具有非平稳性和随机性的特点,提出一种基于经验模态分解(EMD)和人工蜂群算法(ABC)优化支持向量机(SVM)的光伏并网系统输出功率预测模型。首先根据预测日的天气预报数据,构建相似日的15 min输出功率时间序列。然后,将输出功率时间序列进行经验模态分解,得到不同尺度下的固有模态分量IMFn和趋势分量Res,针对每个IMF分量和趋势分量分别建立相应的支持向量机预测模型,并对SVM模型参数进行人工蜂群算法寻优预处理。最后,将每个模型预测的结果进行合成重构,得到光伏并网系统输出功率的预测值。通过实际数据测试表明:基于EMD和ABC-SVM的功率预测模型同单一SVM预测模型及未经优化的EMD-SVM预测模型相比,具有更快的运算速度和更高的预测精度。
According to the output power of PV generation system having the characteristics of non-stationary and randomness, a forecasting model for grid-connected photovoltaic generation system output power is proposed based on EMD and SVM optimized by ABC algorithm. Firstly, the time series data of output power in the similar day with the interval of 15 minutes is built on the basis of weather forecast data of the forecast day. Then, the time series data of output power is decomposed into a series of components including some intrinsic mode components and a trend component under different scales by using EMD, and different SVM forecasting models are built for each intrinsic mode components and trend component, and the parameters of SVM model are optimized by ABC algorithm. Finally, the entire forecasting results are combined into the ultimate forecasting result of grid-connected photovoltaic generation system output power. The forecasting model is tested with the field dada and the results show that the model based on EMD-ABC-SVM has higher accuracy and faster speed compared to single SVM model and EMD-SVM without optimization.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2015年第21期86-92,共7页
Power System Protection and Control
基金
国家自然科学基金项目(U1204515)~~
关键词
光伏并网系统
输出功率预测
模型参数优化
经验模态分解
人工蜂群算法
grid-connected photovoltaic generation system
output power forecasting
parameter optimization of model
empirical mode decomposition
artificial bee colony algorithm
