租赁住房用电智能分析管理数据

数据用于租赁住房用电智能分析管理，可以提升住房的能源效率和用电安全。每月自动分析租赁住房的用电量数据，运用算法模型来检测用电异常情况。当检测到用电异常时，会立即触发两方面的响应机制：
线上智能提示：向租户发送用电异常提醒，提示其检查是否忘记关闭电器等设备，同时推广节能用电的理念，鼓励租户采取节能措施。
线下专业排查：向租赁住房的运营管理团队发送预警消息，择机派遣专业服务人员上门进行实地排查。服务人员将重点检查是否存在电器损坏导致的耗电异常，以及租户是否擅自使用了大功率电器。此外，服务人员还将评估电器使用是否可能引发电线超负荷运行，从而及时预防潜在的安全事故。
通过用电智能分析管理，能够实现对租赁住房用电情况的全面监控和管理，有效提升能源利用效率，确保用电安全。1、数据采集：租赁住房投资建设时安装电表，运营管理系统自动定期采集电表记录数据。2、数据处理：从住房用电量记录数据中，以年月、区域（省市）、住房编号作为唯一键，先提取出当月用电量、上月用电量、去年同期用电量等数据，再计算出当月用电量环比增长率、当月用电量同比增长率、同区域住房当月用电量平均环比增长率、同区域住房当月用电量平均同比增长率等指标，最后通过算法规则进行综合分析。3、算法规则：当月用电量环比增长率=（当月用电量-上月用电量）/上月用电量× 100%；当月用电量同比增长率=（当月用电量-去年同期用电量）/去年同期用电量× 100%；同区域住房当月用电量平均环比增长率=sum(当月用电量环比增长率)/count(住房数量)；同区域住房当月用电量平均同比增长率=sum(当月用电量同比增长率)/count(住房数量)。根据用电异常判定规则判断是否属于异常，如：某住房的当月用电量环比增长率和当月用电量同比增长率的均大于50%，并且当月用电量环比增长率-同区域住房当月用电量平均环比增长率的值和当月用电量同比增长率-同区域住房当月用电量平均同比增长率的值均大于30%，判定该住房用电异常，而不是夏、冬季开空调等正常需求引发的用电量大增。
4、数据应用：租赁住房运营管理系统自动定期查询用电异常数据，有异常数据时，通过短信等方式提示租户某月用电异常，鼓励其自查并采取节能措施；通过工单等方式向运营管理团队派发线下检查任务，检查结果填报到系统。

This dataset is intended for intelligent electricity analysis and management of rental housing, with the goal of enhancing energy efficiency and ensuring electricity safety. It automatically analyzes the electricity consumption data of rental housing on a monthly basis, and utilizes algorithmic models to detect abnormal electricity usage. Once abnormal electricity usage is detected, two response mechanisms will be triggered immediately:
1. Online intelligent notification: Send electricity abnormality reminders to tenants, prompting them to check whether they have forgotten to turn off electrical appliances, promoting the concept of energy conservation, and encouraging tenants to adopt energy-saving measures.
2. Off-site professional inspection: Send early warning messages to the operation and management team of rental housing, and arrange professional service personnel to conduct on-site inspections at an appropriate time. Service personnel will focus on checking whether abnormal power consumption is caused by damaged electrical appliances, whether tenants have privately used high-power electrical appliances, and evaluating whether the use of electrical appliances may lead to wire overloading, so as to timely prevent potential safety accidents.

Through intelligent electricity analysis and management, comprehensive monitoring and management of the electricity usage of rental housing can be realized, effectively improving energy utilization efficiency and guaranteeing electricity safety.
1. Data collection: When a rental housing property is invested in and constructed, electricity meters are installed, and the operation and management system automatically collects the meter recording data at regular intervals.
2. Data processing: Taking the year-month, region (province and city), and housing number as the unique key from the housing electricity consumption record data, first extract data including the current month's electricity consumption, the previous month's electricity consumption, and the electricity consumption in the same period last year. Then calculate indicators such as the month-on-month growth rate of the current month's electricity consumption, the year-on-year growth rate of the current month's electricity consumption, the average month-on-month growth rate of electricity consumption of housing units in the same region in the current month, and the average year-on-year growth rate of electricity consumption of housing units in the same region in the current month. Finally, conduct comprehensive analysis via algorithmic rules.
3. Algorithmic rules:
- Month-on-month growth rate of current month's electricity consumption = (Current month's electricity consumption - Previous month's electricity consumption) / Previous month's electricity consumption × 100%
- Year-on-year growth rate of current month's electricity consumption = (Current month's electricity consumption - Electricity consumption in the same period last year) / Electricity consumption in the same period last year × 100%
- Average month-on-month growth rate of electricity consumption of housing units in the same region in the current month = Sum(Month-on-month growth rate of electricity consumption) / Count(Number of housing units)
- Average year-on-year growth rate of electricity consumption of housing units in the same region in the current month = Sum(Year-on-year growth rate of electricity consumption) / Count(Number of housing units)
Abnormality judgment is conducted based on electricity abnormality determination rules. For example, if both the month-on-month growth rate and the year-on-year growth rate of a certain housing unit's current month's electricity consumption exceed 50%, and the difference between the housing unit's month-on-month growth rate and the average month-on-month growth rate of housing units in the same region, as well as the difference between the housing unit's year-on-year growth rate and the average year-on-year growth rate of housing units in the same region, both exceed 30%, the housing unit is determined to have abnormal electricity usage, excluding sharp increases in electricity consumption caused by normal demands such as using air conditioners in summer and winter.
4. Data application: The operation and management system of rental housing automatically queries abnormal electricity usage data at regular intervals. When abnormal data is detected, send reminders to tenants via SMS or other means to notify them of abnormal electricity usage in a certain month, and encourage them to conduct self-inspection and take energy-saving measures; dispatch offline inspection tasks to the operation and management team via work orders or other channels, and the inspection results will be submitted to the system.

搜集汇总

背景与挑战

背景概述

该数据集为租赁住房用电智能分析管理数据，包含1001条记录，每月更新，用于分析租赁住房的用电情况，提升能源效率和用电安全。数据结构包含年月、区域、住房编号、用电量及相关增长率等13个字段。