混合混沌理论和关联度的神经网络短期电力预报模型的研究Short-term Load Forecasting Model Based on Chaotic Theory,Correlation And Neural Networks
李如琦;孙艳;孙志媛;
摘要(Abstract)：
提出一种将混沌理论、关联度和神经网络相结合的短期负荷预测模型,首先利用混沌理论重构负荷时间序列的相空间吸引子,然后用BP神经网络来拟合空间吸引子的演化,由于使用空间欧氏距离和关联度联合来选取神经网络的训练样本,这样就提高了神经网络对负荷序列混沌特性的联想和泛化推理能力,能够更好的拟合吸引子的演化。实例预测结果表明,本文所提出的预报是有效的、可行的。
关键词(KeyWords)： 混沌理论;短期负荷预测;神经网络;欧氏距离;关联度;嵌入维数
基金项目(Foundation):
作者(Author): 李如琦;孙艳;孙志媛;
Email:
DOI: 10.13335/j.1000-3673.pst.2006.s2.153
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