Building Energy Performance Analytics on Cloud as a Service

被引:2
作者
Lee, Young M. [1 ]
An, Lianjun [1 ]
Liu, Fei [1 ]
Horesh, Raya [1 ]
Chae, Young Tae [1 ]
Zhang, Rui [1 ]
Meliksetian, Estepan [1 ]
Chowdhary, Pawan [1 ]
Nevill, Paul [1 ]
Snowdon, Jane L. [1 ]
机构
[1] IBM Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA
关键词
cloud; energy performance; energy simulation; building energy analytics; visualization;
D O I
10.1287/serv.1120.0040
中图分类号
F [经济];
学科分类号
02 ;
摘要
Reducing energy consumption, improving energy efficiency, and reducing greenhouse gas (GHG) emissions are among the most important initiatives in today's world. Occupied buildings consume a substantial amount of energy, mounting to about 40% of overall energy consumption in most countries. The majority of the world's population either lives or works in buildings; therefore, everybody can contribute in reducing energy consumption, controlling GHG emissions, and mitigating climate change and its potential impact. We developed an analytical tool that can assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, simulating, and optimizing energy consumption in building portfolios. Furthermore, for greater dissemination, we have made this analytic service available on demand in a flexible cloud environment. Cloud is an efficient and effective medium to provide building energy analytics capability to various functions and people in a variety of roles in buildings without investing a substantial amount of money in hardware, software, and information technology infrastructure. We present results of the building energy analytics developed for K-12 public school buildings and a commercial office building complex.
引用
收藏
页码:124 / 136
页数:13
相关论文
共 12 条
[1]  
American Society of Heating Refrigerating and Air-Conditioning Engineers, 2009, 2009 ASHRAE HDB FUND
[2]  
[Anonymous], 2006, Buildings energy data book
[3]  
[Anonymous], 2009, WORLD EN OUTL 2009
[4]   Inverse problems and parameter estimation: integration of measurements and analysis [J].
Beck, JV ;
Woodbury, KA .
MEASUREMENT SCIENCE AND TECHNOLOGY, 1998, 9 (06) :839-847
[5]  
Bobker M, 2011, INT C ENH BUILD OP N
[6]  
Brockwell P., 2013, Time Series: Theory and Methods, ser. Springer Series in Statistics
[7]  
Duncan AJ, 1974, Quality Control Industrial Statistics, Vfourth
[8]  
Kissock J.K., 2003, Transactions-American society of heating refrigerating and air conditioning engineers, V109, P425
[9]  
Lee YM, 2011, P 2011 WINT SIM C PH
[10]  
Liu F, 2011, RC25165 IBM