基于改进最大Lyapunov指数求解的成本预测方法

Cost predicting method on improved largest Lyapunov index

由于传统的Lyapunov指数估算模式中的预测时限要受限于最大Lyapunov指数,在南京汽轮电机集团相关技术人员的大力协助下,对已提出的基于k-t间隔采样混沌模型的求解方法进行了适当地研究和改进,通过引入数据间隔差方法,以得到最佳拟和数据段,有效地提高了预测精度,增加预测时限。在模拟仿真时,与现有的真实数据进行了比较,结果表明取得了较好的预测效果。

Thanks to technical staffs of Nanjing turbine and electric machinery, the k-At interval sampling chaotic model is improved that the data interval difference method which can calculate the best data segment is introduced. Because of the predicting length of the Lyapunov index predicting model is limited by the largest Lyapunov index. And the improved method can improve the precision and increase the predicting length effectively. Compared with the actual data, a better predicting purpose is obtained by the practical power system.