Energy Management For A Large Commercial Building In The Context Of Big Data

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.