商务办公大厦用电分析数据
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商务办公大厦用电分析数据的应用场景是商务办公大厦用电智能监测与管理。通过分析商务办公大厦各个资源点位的用电数据，包括月用电量、月每日用电量均值、标准差、变异系数及当月增长率等，实现了对各处电量使用情况的精准把握，为商务办公大厦管理人员优化能源管理、排查异常能源消耗提供决策支持。此外，其他商务办公大厦可借鉴本分析数据完善自身对于电量使用的监测与管理，由“经验驱动”管理向“数据驱动”管理转变；还可将自身用电数据与本用电分析数据进行对标分析，定位自身电量使用及管理水平，为节能提升、运营优化及模式借鉴提供重要依据。1. 数据来源 通过商务办公大厦内部各个资源点位的电表获取各个资源点位的每日用电数据，包括建筑类型、电表点位、用电时间、日用电量等，并存储到对应数据库中。 2. 数据预处理 以商务办公大厦电表点位作为唯一标识，在数据库中筛选记录，提取出各个资源点位的日用电量等信息，并通过以下算法规则进行数据分析。 3. 数据分析 商务办公大厦用电分析数据算法规则： 对各个资源点位的日用电量按所属月份进行加和计算，得到月用电量。 对月用电量加以运算，得到月每日用电量均值，月每日用电量均值=月用电量/当月天数；再对当月每日用电量求取标准差和变异系数，月每日用电量标准差用于监测各个资源点位当月用电的波动情况，月每日用电量变异系数=月每日用电量标准差/月每日用电量均值，消除了量纲影响，便于对各个资源点位进行用电稳定性的横向比较。 在各月用电量数据的基础上，计算当月用电增长率，当月用电增长率=（月用电量-上月用电量）/上月用电量。 各个资源点位中，当月用电增长率大于100%为重点关注资源点位，说明该点位用电需求愈发旺盛，需要警惕异常用电消耗；增长率为负数，且绝对值大于50%以上，说明该点位用电明显减少，需要跟进深入了解情况。
This commercial office building electricity consumption analysis dataset is applied to intelligent monitoring and management of electricity usage in commercial office buildings. By analyzing the electricity consumption data of various electric meter points in commercial office buildings, including monthly electricity consumption, average daily electricity consumption per month, standard deviation, coefficient of variation, and monthly growth rate, etc., the system can accurately grasp the electricity usage status of each area, providing decision support for building managers to optimize energy management and identify abnormal energy consumption. In addition, other commercial office buildings can refer to this analysis dataset to improve their own electricity usage monitoring and management, and transform their management mode from "experience-driven" to "data-driven". They can also conduct benchmarking analysis between their own electricity consumption data and this dataset, to evaluate their own electricity usage and management levels, providing an important basis for energy conservation improvement, operation optimization and mode reference. 1. Data Source Daily electricity consumption data of each electric meter point is collected through the meters installed at various resource points inside the commercial office building, including building type, meter point, electricity consumption time, daily electricity consumption, etc., and stored in the corresponding database. 2. Data Preprocessing Taking the electric meter points of commercial office buildings as unique identifiers, records are screened in the database, and information such as daily electricity consumption of each resource point is extracted, and data analysis is carried out according to the following algorithm rules. 3. Data Analysis Algorithm Rules for Commercial Office Building Electricity Consumption Analysis Data: 1. Sum the daily electricity consumption of each resource point according to their respective months to obtain the monthly electricity consumption. 2. Perform calculations on the monthly electricity consumption to obtain the average daily electricity consumption per month, which is calculated as Monthly Electricity Consumption / Number of Days in the Current Month; then calculate the standard deviation and coefficient of variation of the daily electricity consumption of the current month. The daily standard deviation of electricity consumption is used to monitor the fluctuation of electricity usage of each resource point in the current month, and the daily coefficient of variation of electricity consumption is calculated as Daily Electricity Consumption Standard Deviation / Average Daily Electricity Consumption per Month, which eliminates the influence of measurement units and facilitates horizontal comparison of electricity usage stability among various resource points. 3. Based on the monthly electricity consumption data, calculate the monthly electricity growth rate, which is calculated as (Monthly Electricity Consumption - Previous Month's Electricity Consumption) / Previous Month's Electricity Consumption. 4. Among all resource points, those with a monthly electricity growth rate exceeding 100% are key monitoring points, indicating that the electricity demand of this point is increasing and abnormal electricity consumption should be alerted; those with a negative growth rate and an absolute value greater than 50% show a significant reduction in electricity usage at this point, requiring further in-depth investigation.
