| 引用本文: | 李广,邹德忠,谈顺涛.基于混沌神经网络理论的小电网短期电力负荷预测[J].电力自动化设备,2006,(2): |
| .Short-term load forecast for small power net based on chaos-artificial neural network theory[J].Electric Power Automation Equipment,2006,(2): |
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| 摘要: |
| 通过对小电网负荷数据的特点分析,将时间序列处理、混沌理论和神经网络理论相结合提出了一种基于混沌神经网络理论的电力负荷预测模型。利用Matlab对实际数据进行了仿真计算。通过实例计算,并和不用相空间重构的神经网络的负荷预测算法的各种误差指标的分析比较说明,利用相空间重构对历史数据序列进行拆分或重构可以提高负荷预测的精度。 |
| 关键词: 短期负荷预测,非线性理论,混沌理论,相空间重构,神经网络,时间序列 |
| DOI： |
| 分类号:TM714 |
| 基金项目: |
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| Short-term load forecast for small power net based on chaos-artificial neural network theory |
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LI Guang ZOU De-zhong TAN Shun-tao
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| Abstract: |
| According to the summarized features of load data in small power net,a chaos-artificial neural network theory based on load forecast model is presented,which combines time series processing,chaos theory and neural network theory together.Actual data are used for forecast simulation with Matlab.The forecast error indices of neural network with and without phase space reconstruction are compared through computation.Results prove that using phase space reconstruction to split and reconstruct the historical data can improve the precision of load forecast. |
| Key words: short-term load forecast,nonlinear theory,chaos theory,phase space reconstruction,artificial neural network,time series |
