最大Lyapunov指数短期电力负荷预测模型仿真

Largest Lyapunov exponent of short-term electric load forecasting model simulation

已有的研究工作表明,针对Lyapunov指数预报模式的预测时限受负荷吸引子最大Lyapunov指数的限制,已提出的k-△t间隔采样混沌模型在短期电力负荷预测中能有效的提高负荷预测精度,增加预测时限。对k-△t间隔采样混沌模型中求解最大Lyapunov指数的方法进行了改进,对小数据量法产生的数据,引入数据间隔差方法求出最佳拟和数据段。利用VC6.0设计了仿真软件,对某实际电网进行了短期负荷预测,试验结果表明,能有效提高负荷预测精度。

It has been proven by the current research work, the forecasting length of the Lyapunov exponent forecasting model is limited by the largest Lyapunov exponent of the load attractor. The k-△t interval sampling chaotic model presented could improve the precision and increase the forecasting length in the short-term electric load forecasting. The method of calculating the largest Lyapunov exponent is improved. And based on the small data method, the data interval difference method which could calculate the best data segment is introduced. Corresponding software is developed in VC6.0 and is used to forecast the short-term load of a practical power system. The numerical experiments demonstrate that the forecast precision is improved.