医药化工企业综合能耗分析数据

本项数据服务主要针对于医药化工企业，通过能源采集系统对水电表的数据统计，根据能耗历史数据计算分析，有效分析企业能耗数据。医药化工企业通过本项数据服务，可以清晰了解自身存在的能耗问题，寻找改善的空间，针对性地提出增质提效方案，目前应用平台数据服务的各大医药化工企业均产生了效益的提升。该项数据服务可向整个医药化工行业推广，对医药化工企业提升产能效率、避免能耗浪费有非常明显的意义，也为各级党委、政府倡导的节能减排、能源优化提供支持。1.数据收集：运用大数据平台，对医药化工行业各企业相关能耗数据进行采集并汇总，对部分数据和字段进行脱敏清洗。
2.数据运算：定义第i次统计数据后，能耗系数为X(i)，则能耗系数平均值Xa(i)=（X(1)+X(2)+X(3)+…+X(i)）/i；标准误差S=STDEVP*Xa(i)/SQRT(COUNT*Xa(i))，其中STDEVP、SQRT、COUNT均为计算机的命令函数；标准能耗下限W(min)=Xa(i)-1.96*S；标准能耗上限W(max)=Xa(i)+1.96*S。
3.数据分析：将近10次统计的能耗系数,即X(i-5)、X(i-4)、X(i-3)、X(i-2)、X(i-1)、X(i+1)、X(i+2)、X(i+3)、X(i+4)、X(i+5)，和计算所得的标准能耗上下限进行比较分析。近10次统计的能耗系数必须均在[W(min),W(max)]区间内，分析结果才能为“合格”；只要有一次不在该区间内，分析结果均为“不合格”。

This data service is primarily targeted at pharmaceutical and chemical enterprises. It collects and counts data from water and electricity meters via an energy acquisition system, and conducts calculation and analysis based on historical energy consumption data to effectively evaluate enterprises' energy consumption status. By using this data service, pharmaceutical and chemical enterprises can clearly identify their existing energy consumption problems, find room for improvement, and formulate targeted plans to enhance quality and efficiency. Currently, all major pharmaceutical and chemical enterprises that have adopted the platform's data service have achieved improvements in their operational benefits. This data service can be promoted across the entire pharmaceutical and chemical industry. It is of great significance for helping enterprises improve production efficiency and avoid energy waste, and also provides support for the energy conservation, emission reduction and energy optimization initiatives advocated by Party committees and governments at all levels.
1. Data Collection: Utilize a big data platform to collect and aggregate relevant energy consumption data of enterprises in the pharmaceutical and chemical industry, and perform desensitization and cleaning on partial data and fields.
2. Data Calculation: After defining the i-th statistical data, let the energy consumption coefficient be X(i). Then the average energy consumption coefficient Xa(i) = (X(1) + X(2) + X(3) + … + X(i)) / i; the standard error S = STDEVP * Xa(i) / SQRT(COUNT * Xa(i)), where STDEVP, SQRT and COUNT are all computer command functions; the standard lower limit of energy consumption W(min) = Xa(i) - 1.96 * S; the standard upper limit of energy consumption W(max) = Xa(i) + 1.96 * S.
3. Data Analysis: Compare and analyze the energy consumption coefficients of the latest 10 statistics, namely X(i-5), X(i-4), X(i-3), X(i-2), X(i-1), X(i+1), X(i+2), X(i+3), X(i+4), X(i+5), against the calculated standard upper and lower limits of energy consumption. The analysis result will be "Qualified" only if all the 10 latest energy consumption coefficients fall within the interval [W(min), W(max)]; if any one of them is outside this interval, the analysis result will be "Unqualified".

搜集汇总

数据集介绍

特点

该数据集包含医药化工企业的综合能耗数据，涵盖854条记录，每日更新，用于分析企业能耗问题并提升效率。数据集提供了详细的能耗系数计算和分析方法，支持医药化工行业的节能减排和能源优化。