基于动量因子的神经网络群电流负荷预测模型

A forecasting model for current load of neural network group based upon momentum factor

通过建立改进的4层神经网络群,以历史负荷电流作为样本进行训练,实现对于未来负荷电流的预测。针对传统BP神经网络易收敛到局部极值的问题,引入了动态调整的动量因子。为增强对于随月份动态变化较剧烈的负荷的预测能力,提出了BP网络群结构。数据模拟结果说明该算法具有高精确性,可有效估算出下一阶段线路电流负荷变化趋势值,并且预测速度满足实际使用要求。该模型可以用于监测重点单位用电负荷变化情况,及早提示供电单位采取相应措施,促进智能电网建设。

Using the former actual line current load operation value as the training sample, the improved four layer neural network group model is put forward to predict the future current load value. For the problem that BP neural network is easy to converge to a local extremum, automatic adjusting momentum is applied. To enhance the ability in forecasting the load changing a lot in different months, the BP network group structure is put forward. Data simulation results show that the algorithm has high accuracy and can effectively estimate the current load change trend of the next time. The speed of prediction can meet the requirements of practical application. This model can be used as a large data analysis model for monitoring the change of the power load of the key units, and the early proposal is promptly proposed to power supply unit to take the corresponding measures. This model can also promote the construction of smart grid.