摘要
采用混沌理论分析方法,对燃气负荷时间序列进行了相空间重构,通过计算关联维数和最大李亚普诺夫指数判定燃气负荷具有混沌的性质.在此基础上,分别采用基于混沌理论的加权一阶局域法、最大李亚普诺夫指数法和贝叶斯正则化神经网络模型对城市燃气日负荷进行了预测.实例预测结果表明,混沌时间序列分析方法可应用于燃气负荷预测研究,特别是结合了混沌理论、神经网络与贝叶斯正则化方法各自优点的神经网络模型取得了较好的预测效果.
The urban gas consumption time series was analyzed with phase space reconstruction based on chaos theory.The chaotic characters of urban gas consumption were identified by calculating the correlation dimension and largest Lyapunov exponent.Then,several methods including weighted one-rank local-region method,largest Lyapunov exponent method and Bayesian regularization neural network model were applied on forecasting of daily urban gas consumption.The test results indicate that the chaotic time series analysis method is feasible to be used in urban gas consumption forecasting.Combined with the advantages of chaos theory,neural network and Bayesian regularization method,the forecasting performance of Bayesian regularization neural network model based on phase space reconstruction is especially good.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第10期1511-1515,共5页
Journal of Tongji University:Natural Science
基金
教育部高等学校博士学科点专项科研基金资助项目(20050247010)
关键词
燃气负荷
燃气供应
混沌理论
相空间重构
预测
gas consumption
gas supply
chaos theory
phase space reconstruction
forecasting
