Short Time Load Forecasting 短期负荷预测
super short time load forecasting 超短期负荷
super short-time load forecasting 超短期负荷预测
online super short-time load forecasting 在线超短期负荷预测
In application of neural networks based short-term load forecasting model, the main problems are over many training samples, thus resulting long training time and slow convergence speed.
在神经网络负荷预测实际应用中，突出的问题是训练样本大、训练时间长、收敛速度慢。
The principle of extension short-time load forecast was put forward, that is forecasting unknown load point by adding information of new load point to the point, the load was known.
提出了扩展短期负荷预测的原理，即在已知当日部分负荷的条件下，引入最新获得的负荷相关信息，预测当日未知的多点负荷。
A new method of short time load forecasting on the base of elasticity coefficient is put forward according to the characteristic of obvious fractal self similarity of load change in power system.
针对电力系统负荷变化具有明显的分形自相似性的特点，提出了一种新的基于弹性系数的短期负荷预测方法。
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