基于混沌理论和BP 神经网络的某基地电力短期负荷预测

为了合理安排并优先保证军事基地中的电力调度问题，提出一种基于混沌时间序列和BP神经网络相结合的电力短期负荷预测方法。根据混沌理论及神经网络方法，先基于延迟坐标相空间重构技术，再应用互信息法和饱和关联维数法，选择延迟时间石和嵌入维数m，然后用BP神经网络来实现预测，并通过对海军某基地的电网的时间负荷序列进行实测仿真。仿真结果表明：相对误差均在5％vX内，且有33．3％的误差在1％以内，证明该预测方法具有较高的预测精度和应用价值。

Short-Term Load Forecasting of Navy Certain Base Based on Chaotic Time Series and Artificial Neural Networks

In order to rationally arrange and give priority to ensuring the power dispatching problem of a military base,a method of shot-term load forecasting based on chaotic time series and artificial neural networks is presented.According to chaos theory and neural networks method,it is based on the delay coordinates phase space reconstruction first,choose time delay ’ ’ and embedding dimension ’m’ by using the method of mutual information and saturation correlation dimension after,then use the BP neural networks prediction,carry out an experimental simulation about grid in the period of load sequence on a base finally.The simulation results show that the relative errors is within 5%,and 33.3% of the error is less than 1%,proved that the prediction method has higher forecasting precision and application value.