摘要
运用复杂系统理论分析电力系统.采用聚类方法对用电区域进行子系统划分,并通过改进的神经元网络算法和增加天气因素的预测方法进行短期负荷预测.通过算例和电力系统应用,证实了该算法的可行性,较显著地提高了负荷预测的准确率.
Power network is a complex nonlinear system.The complex system analysis theory was applied to analyze the power network and divide it into some sub-systems according to its cluster analysis.Then back profagation neural network incorporated with weather influence factors was used to forecast the power network short-term load.Experimental results verify that the method is helpful to improve the forecasting accuracy.
出处
《上海理工大学学报》
CAS
北大核心
2011年第1期39-43,共5页
Journal of University of Shanghai For Science and Technology
基金
上海市研究生创新基金资助项目(JWCXSL0902)
关键词
复杂系统
短期负荷预测
聚类分析
神经元网络
complex systems
short-term load forecast
cluster analysis
neural network
