| 基于混沌时间序列和神经网络的电力短期负荷预测 |
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| 引用本文： | 李如琦 孙艳 孙志媛. 基于混沌时间序列和神经网络的电力短期负荷预测[J]. 电工标准与质量, 2006, 21(4): 56-59 |
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| 作者姓名： | 李如琦 孙艳 孙志媛 |
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| 作者单位： | 广西大学电气工程学院 广西南宁530004 |
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| 摘 要： | 提出一种将混沌时间序列和神经网络相结合的短期负荷预测方法,利用混沌理论重构相空间的吸引子,然后用BP神经网络来拟合空间吸引子的演化,同时利用空间欧氏距离来选取神经网络的输入样本,实例预测结果表明所提出方法的有效性和可行性.
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| 关 键 词： | 混沌时间序列 短期负荷预测 神经网络 欧氏距离 嵌入维数 |
| 文章编号： | 1006-7140（2006）04-0056-04 |
| 收稿时间： | 2006-09-07 |
Short-term Load Forecasting Based on Chaotic Time Series and Neural Networks |
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| LI Ru-qi, SUN Yan, SUN Zhi-yuan. Short-term Load Forecasting Based on Chaotic Time Series and Neural Networks[J]. Journal of Changsha University of Electric Power(Natural Science Edition), 2006, 21(4): 56-59 |
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| Authors: | LI Ru-qi SUN Yan SUN Zhi-yuan |
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| Affiliation: | School of Electrical Engineering, Guangxi University, Nanning 530004, China |
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| Abstract: | A method of short-term load forecasting based on chaotic time series and neural networks is presented in this paper.Firstly,attractors in phase spaces using chaotic theory is reconstructed.Secondly,the attractor's evolvement using BP neural networks is made,and the neural network's input data using Euclid distance is selected.The result analysis of the practical examples show that the proposed method is effective and feasible. |
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| Keywords: | chaotic time series short-term load forecasting neural network Euclid distance embedding dimension |
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