米特电能表失准算法训练测试数据集

电能表失准算法的输入数据体系包含开关节点总表用电量与该开关下所有子电表计量数据，两类数据需经过时间对齐与异常值清洗，形成结构化数据集。算法通过构建总 - 分电量线性回归模型，计算子电表计量值与理论分摊值的偏差率，输出各子电表的失准比例，并标注失准类型。该过程可定位多电表计量偏差，为电表更换、接线核查或误差校准提供数据支撑，确保 “总 - 分” 电量逻辑一致性，提升能源计量网络的整体准确性。

The input data system for the electricity meter misalignment algorithm includes the total power consumption of the switch node main meter and the metering data of all sub-meters under this switch. These two types of data need to undergo time alignment and outlier cleaning to form a structured dataset. The algorithm constructs a total-branch power linear regression model, calculates the deviation rate between the sub-meter metering values and the theoretical apportioned values, outputs the misalignment ratio of each sub-meter, and marks the misalignment type. This process can locate metering deviations among multiple meters, provide data support for meter replacement, wiring inspection or error calibration, ensure the logical consistency of total-branch power consumption, and improve the overall accuracy of the energy metering network.

搜集汇总

背景与挑战

背景概述

该数据集专为电能表失准算法设计，用于训练和测试模型以精准识别电表计量故障。它基于开关总表与子电表用电数据，通过线性回归和机器学习技术计算偏差率，输出失准比例和类型，支持电力计量管理、能耗监测和智能电网运维等应用场景。数据集以Excel格式提供，旨在提升能源计量网络的整体准确性和可靠性。