数字化电力运营监控平台设计模型

Digital power marketing operation monitoring platform design model

为有效提升电力企业的工作效率和市场竞争力,提出了数字化电力运营监控平台的设计方案。基于K均值聚类算法对电力异常数据进行检测,并通过图形展示异常数据检测结果,红色的深浅表示预警的等级。提出了基于灰色神经网络的用户需求预测模型,并将灰色神经网络模型和灰色模型、BP神经网络模型对比,验证了灰色神经网络预测模型具有更高的预测精度。采用灰色神经网络模型预测了某地区用户电量需求,为电力系统的高效调度提供了数据支撑。所设计的数字化电力运营监控平台对提升电力企业市场竞争力具有一定的实用价值。

In order to effectively improve the work efficiency and market competitiveness of power enterprises,the design scheme of digital power operation monitoring platform was proposed.Based on the K-means clustering algorithm,the abnormal power data was detected,and the abnormal data detection results were displayed graphically,and the shade of red indicates the level of early warning.A user demand prediction model based on grey neural network was proposed,and the grey neural network model was compared with grey model and BP neural network model,which verified that the grey neural network prediction model had higher prediction accuracy.The grey neural network model was used to predict the power demand of users in a certain region,which provided data support for the efficient dispatching of power system.The designed digital power operation monitoring platform had certain practical value to enhance the market competitiveness of power enterprises.