[1]丁红卫,王文果,万 良,等.基于 BP 神经网络的电网物资需求预测研究[J].计算机技术与发展,2019,29(06):138-142.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 029]
　DING Hong-wei,WANG Wen-guo,WAN Liang,et al.Research on Material Demand Prediction of Power Network Based on BP Neural Network[J].,2019,29(06):138-142.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 029]
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基于 BP 神经网络的电网物资需求预测研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年06期
页码:
138-142
栏目:
应用开发研究
出版日期:
2019-06-10

文章信息/Info

Title:
Research on Material Demand Prediction of Power Network Based on BP Neural Network
文章编号:
1673-629X(2019)06-0138-05
作者:
丁红卫1 ; 王文果2 ; 万 良1 ; 罗 剑2
1. 贵州大学 计算机科学与技术学院,贵州 贵阳 550025; 2. 贵州电网有限责任公司物流服务中心,贵州 贵阳 550025
Author(s):
DING Hong-wei1 ; WANG Wen-guo2 ; WAN Liang1 ; LUO Jian2
1. School of Computer Science and Technology,Guizhou University,Guiyang 550025,China; 2. Logistics Service Center of Guizhou Power Grid Limited Liability Company,Guiyang 550025,China
关键词:
电网物资预测; BP 神经网络; Adam 优化算法; 过度拟合
Keywords:
power grid materials forecast; BP neural network; Adam optimization algorithm; over-fitting
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 06. 029
摘要:
目前电网公司对电网物资的预测和研究存在诸多问题,如缺少科学的指导、合理的依据及忽视设备数据之间存在的关联等。 针对贵州电网建设项目物资的需求特点,建立了 BP 神经网络物资预测模型。 文中通过使用 Adam 优化算法代替传统 BP 神经网络所使用的随机梯度下降算法,有效避免了因随机梯度下降算法易于陷入局部最优而导致预测误差较大的问题,并加入 L2 正则化方法来防止 BP 神经网络因训练样本较少或过度训练而导致的过度拟合现象的发生。 通过所需设备的历史数据对构建的 BP 神经网络模型进行训练,然后将训练好的模型用于电网物资的需求预测。 通过实验显示,改进的 BP 神经网络模型用于电网物资的预测,能够显著地减少电网物资需求预测的误差。
Abstract:
At present,there are many problems in the prediction and research of power grid materials by power grid companies,such as lack of scientific guidance,reasonable basis and neglect of the connection between equipment data and so on. According to the demand characteristics of Guizhou power grid construction project,we establish a BP neural network material prediction model. The Adam optimization algorithm is used to replace the stochastic gradient descent algorithm used in the traditional BP neural network,which effectively avoids that the stochastic gradient descent algorithm is prone to fall into the local optimum and the prediction error is large. The L2 regularization method is added to prevent the over-training of BP neural network caused by less training samples or overt-fitting. The BP neural network model is trained by the historical data of the material demand,and then the trained model is used to forecast the demand of power grid materials. The experiment shows that the improved BP neural network model can significantly reduce the error of power grid material demand prediction.

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更新日期/Last Update: 2019-06-10