大数据背景下的某大型商业建筑能源管理

【作者】 朱明；

【导师】 宋哲；

【作者基本信息】 南京大学 ， 工商管理（MBA）（专业学位）， 2017， 硕士

【摘要】 日益增长的能源消耗和环境问题对政府和商业机构运行管理人员提出更高的能源管理要求。能源管理是运营管理的一部分,本文从环境和能耗数据出发,提出基于大数据分析的大型公共商业建筑能源管理,是一种数据实证上的运营管理研究。商业建筑的能耗受到环境因素(季节、气象、用户行为等)影响,但相关关系比较复杂,木次研究尝试对这些环境因素对能耗影响做定性和定量分析。作者对某大型公共商业建筑的环境数据和能耗数据(以电力负荷为代表)进行了搜集和整理,比较了当前主流建筑能源模型方法,结合木次研究目的和数据特点,选择线性回归分析作为分析一工具。论文选取研究中的一元回归、二元回归和部分五元回归的分析结果和统计解释,对环境变量对商业建筑能耗的影响做出定量分析,以此为依据提出运营管理方面的建议。这些建议对同地区、同类型大型商业建筑的能耗分析、负荷预测以及能源管理工作有指导借鉴意义。同时本次研究的分析方法和结论有助于政府和电力主管部门进行大型公共商业建筑负荷预测和针对此类电力用户的电力需求侧定制化管理。通过线性回归模型研究,根据季节、气象环境变化、以及工作日、节假日对商场负荷的影响的定量分析,分析结果对商业建筑运营管理具有指导意义:包括能源使用、商场综合资源配置和管理考核。1)能耗(负荷)预测值和实际能源消耗的明显不一致有可能是能源管理的漏洞,管理者应检查能源使用的跑冒滴漏,发掘节能降耗的空间。2)分析结果给出运营管理的重要实证依据,数据分析得出的结论具有实证性,可以作为其它运营管理数据的校验,可以帮助发现经营管理、甚至是政府监管方面的漏洞和优化商业建筑的资源配置。3)能源大数据分析结果完善了现有运营管理手段和考核手段。反映在两方面:i、原始数据从粗糙统计向精细化管理升级;ii、能源运营管理从面向结果发展到面向过程。过去关于能源管理和考核都是基于事后结果的粗糙数据,即使准确,能源浪费和事故也是已经发生。而本次的研究结果和二工具可以给管理者提供高度量化和精细化的运营过程考核指标和手段,使能源浪费和潜在事故的及时干预成为可能。本文研究的模型和结论为电力主管部门提供了有效的电力需求侧管理工具,可以为同类商业建筑个性化定制需求响应机制、实施触发条件和效果评价方案。更多还原

【Abstract】 The energy consumption of commercial buildings has risen steadily in recent years,which has challenged the government and operation management in business iinstitutions.This paper presents a research of energy management,which is a part of operational management,driven by operation date of a large puclic comercial building,which is an empirical-data kind of operation management.Expected energy loads,transportation,and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings.However,technology is now available that can accurately monitor,collect,and store the huge amount of data involved in this process.Furthermore,this technology is capable of analyzing and exploitng such data in meaningful ways.Not surprisingly,the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest.This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management,especially in the building sector.The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.By linear regression analysis,qualitative and quantitative analysis are both included to determine the influence on building energy consumption by circumstance factors,such as season,climate and user behavior,which results in operational recommendations to building managers including optimizations on energy management and on deploying sales resouces,such as below:1)The difference between the load prediction and real load data may be caused by flaws of energy management and the operation manager ought to check it to find out the opportunities of reduction of energy consumption and energy saving.2)The data analysis model presented in the paper renders an empirical result and a utility to check other management data,which facilitate operational optimization.3)The energy big data method and its result improves the means of management and assessment by i,analysis upgrading from rough data statistics to fine management;ii,energy operation shifting from result-oriented management toward process-oriented.The method and result derived from the paper gives the power administration a utility of Demand Side Management,which can help to customize demand response mechanism,trigger condition and effect evaluation for commercial buildings of the same kind.更多还原