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基于动量因子的神经网络群电流负荷预测模型

发布时间:2019-04-01 06:44
【摘要】: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。
[Abstract]:By establishing an improved 4-layer neural network group and training the historical load current as a sample, the prediction of the future load current can be realized. The momentum factor of dynamic adjustment is introduced to solve the problem that the traditional BP neural network is easy to converge to local extremum. In order to enhance the forecasting ability of load with dynamic change with month, the structure of BP network cluster is proposed. The simulation results show that the proposed algorithm has high accuracy and can effectively estimate the trend value of line current load variation in the next stage, and the prediction speed can meet the requirements of practical application. The model can be used to monitor the change of power load in key units and prompt the power supply units to take appropriate measures to promote the construction of smart grid.
【作者单位】: 浙江大学电气工程学院;
【分类号】:TM715;TP183

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