摘要
针对光伏系统输出功率的波动性和间歇性特点,提出一种基于主成分分析(PCA)和遗传算法(GA)优化的BP神经网络功率短期预测方法。通过历史功率数据和实时气象因素对输出功率进行直接预测,利用主成分分析法将多个原始变量降维成少数彼此独立的变量,作为神经网络的输入。同时利用遗传算法的全局搜索特性在解空间中定位一个较好的空间,优化BP的初始权值阈值,克服了传统BP神经网络易陷入局部极小点、学习收敛速度慢的问题。通过建立不同预测模型进行对比,验证了所提算法和模型的有效性。
In view of the fluctuation and the intermittence of the output power of the photovoltaic system, based on principal component analysis（PCA） and genetic algorithm（GA） optimization, a short term forecasting method of BP neural network power is proposed. Direct forecasting of output power is done by historical power data and real time meteorological factors. It uses principal component analysis to reduce the dimension of multiple original variables into a few independent variables, so that it can optimize the initial weights of back-propagation＇s threshold and overcome the traditional BP neural network easy to fall into local minimum point, and the problems of slow convergence speed. The results of the comparison for different forecast models validate the effectiveness of the algorithm and proposed model.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2016年第22期90-95,共6页
Power System Protection and Control
基金
辽宁省自然科学基金项目(2013020141)~~
关键词
主成分分析
遗传算法
功率预测
BP神经网络
光伏系统
principal component analysis
genetic algorithm
power forecasting
BP neural network
photovoltaic system
