时用水量观测序列的最大Lyapunov指数及其预测时间尺度

在传统wolf最大Lyapunov指数算法的基础上,提出新旧向量转变考虑长度及角度权重搜索的改进wolf计算方法.利用提出的改进算法对杭州市时用水量观测序列的混沌特性及其最大可预测时间尺度问题进行了探讨.为求比较,文中还分别对1小时和24小时两种时间间隔的用水量序列进行了最大Lyapunov指数计算.结果表明:1时用水量系统中存在明显的混沌特性;224时间隔的序列具有长于连续时序列的最大预测尺度.以上两条性质的提出对城市时用水量的较好预测具有其现实意义.

The Largest Lyapunov Exponent and the Maximum Predictable Time Scale of Hourly Series in Urban Water Consumption

Based on conventional Wolf's algorithm for largest Lyapunov exponent, an improved calculation method was put forward in which the search for new vector length and its evolution angle weights. The chaotic characteristics and maximum predictable time scale of the observed series of hourly water consumption in Hangzhou were discussed using the advanced algorithm. For comparison, the largest Lyapunov exponents of water consumption series of one-hour and 24-hour intervals were calculated respectively. The results indicate the followings: first, chaotic characteristic obviously exists in the hourly water consumption system; second, 24-hour interval series has the maximum predictable scale longer than hourly series. The above two properties have practical significance for better prediction of urban hourly water consumption.