摘要
基于混沌理论对电力负荷的复杂时间序列进行分析 ,得出该时间序列属于混沌序列的结论 ,就此提出了一种新的神经网络 ( NN)学习算法——混沌学习算法。该算法中的混沌轨道的游动性有利于系统跳出局域极值的束缚而寻求全局最优 ,这样克服了前馈 NN的 BP学习算法所存在的本质问题 ,使 NN训练的收敛性好、速度快、误差小。文中通过对实际系统负荷预测结果 ,与 BP算法预测结果比较 ,证明了混沌学习算法的电力负荷短期预测具有明显好的效果。
This paper analyzes the time series of power load using chaotic theory. then concludes that power load time seriesbelongs to chaotic series.' A new neural network (NN) learning algorithm, chaotic learning algorithm, is proposed in thispaper. This algorithm has similar ability with random search approach so that the walking of chaotic track can escape fromthe local extremum and reach the global optimum. The actual forecasting results and comparison with the BP algorithm provethat chaotic learning algorithm has better forecasting accuracy and convergence.
出处
《电力系统自动化》
EI
CSCD
北大核心
2000年第2期32-35,共4页
Automation of Electric Power Systems
关键词
神经网络
负荷预测
混沌学习算法
电力负荷
neural network: load forecasting
chaotic learning algorithm: Lyapunov exponents: phase space reconstruction