居民自用充电桩负荷异常数据集

首先，选取具有典型用电特征的居民自用充电桩用户，从用电地址、负荷曲线波形、日最大负荷值、最小负荷值、负载率、用电需求规律、关联居民用电户电量变化等维度建立信息标签，梳理其负荷特性，实现对目标客户用电情况的精细化、多维度画像，分析预测高频风险点。进一步，基于熵权法的分析算法，得出负载率、用电地址相关度、关联户负荷变化等多项指标权重，构建充电桩用电负荷异常模型，为多指标综合评价提供理论依据。再利用机器学习方法，搭建历史数据训练预测模型，以提升充电桩异常数据智能分析的准确率。最后根据各项异常数据管控要点，将主题逻辑代码嵌入营销2.0系统，以卡片方式进行可视化主题展示，数据分析成果可应用于线上派单和现场检查。营销2.0系统中以月为周期自动生成用电异常预警，可“一键生成”线上管控工单，巡检人员可依据工单信息于现场精准挖掘线索，快速定位异常问题并及时反馈，有效提升检查效率。

First, select residential private charging pile users with typical electricity consumption characteristics, establish information labels from dimensions such as electricity consumption address, load curve waveform, daily maximum load value, daily minimum load value, load rate, electricity consumption demand pattern, and electricity consumption changes of associated residential households, sort out their load characteristics, achieve refined and multi-dimensional portraits of the target customers' electricity consumption status, and analyze and predict high-frequency risk points. Further, based on the entropy weight method analysis algorithm, calculate the weights of multiple indicators including load rate, electricity consumption address correlation, and load changes of associated households, and construct a charging pile electricity load anomaly model to provide a theoretical basis for multi-index comprehensive evaluation. Furthermore, use machine learning methods to build a historical data-trained prediction model to improve the accuracy of intelligent analysis of charging pile abnormal data. Finally, based on the key points of abnormal data control, embed the thematic logic code into the Marketing 2.0 system, and carry out visual thematic display in the form of cards. The data analysis results can be applied to online dispatching and on-site inspections. The Marketing 2.0 system automatically generates electricity consumption anomaly alerts on a monthly basis, and can "one-click generate" online control work orders. Inspectors can accurately dig clues on-site based on the work order information, quickly locate abnormal problems and provide timely feedback, effectively improving inspection efficiency.

搜集汇总

数据集介绍

特点

该数据集用于居民自用充电桩的用电监控和异常分析，通过多维度信息标签和机器学习方法构建异常模型，提升智能分析的准确率，并应用于线上派单和现场检查。