在用混沌理论和神经网络进行短期负荷预测时，神经网络的输入的选择至关重要，该程序用matlabl实现了基于混沌时间序列的嵌入维数的选择-using chaos theory and neural networks for short-term load forecasts, the neural network is essential to choose an input, The procedure used matlabl achieved a chaotic time series bas

本程序时基于混沌理论和ELMAN神经网络的短期负荷预测，能取得很好的预测效果，直接使用该程序就能实现电力短期负荷预测，同样使用于其他类型的时间序列预测-the procedures based on chaos theory and neural networks ELMAN short-term load forecasts, can be achieved very good results forecast, the direct use of the procedure we will

基于平移不变小波变换的电力时间序列预测，结合多层感知器（MLP）神经网络， 预测精度很好。-Electricity demand time-series forecasting program Based on shift-invariant wavelet transform features