基于混沌理论和RBF神经网络的短期负荷预测

Zhenzhen Yuan, Shuang Liu, Linyan Xue, Xiu-e Yuan
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引用次数: 2

摘要

电力系统负荷是一个非线性的时间序列,针对电力系统负荷的复杂性和非线性,本文结合混沌理论的思想,充分利用数据在相位空间重构电力负荷的基础上对负荷进行预测,由于神经网络具有优越的逼近能力和预测能力,利用基于RBF神经网络的方法和Matlab仿真,仿真结果表明,这样的预测算法获得了良好的效果。
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Short-term load forecasting based on chaos theory and RBF neural network
Power system load is a nonlinear time series, for the complexity and nonlinear of power systems loads, this paper combines the idea of chaos theory, make full use of data in the reconstruction phase space power load based on the load of forecast, due to the approximation capability of neural networks with superior predictive ability, the use of RBF neural network-based method and Matlab simulation, the simulation shows that such a prediction algorithm to obtain good results.
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