Applying science and mathematics to big data for smarter buildings

Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science a...

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Vydané v:Annals of the New York Academy of Sciences Ročník 1295; číslo 1; s. 18 - 25
Hlavní autori: Lee, Young M., An, Lianjun, Liu, Fei, Horesh, Raya, Chae, Young Tae, Zhang, Rui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Blackwell Publishing Ltd 01.08.2013
Wiley Subscription Services, Inc
Predmet:
Air Pollution - prevention & control
Air Pollution - statistics & numerical data
analytics
Building management systems
Buildings
data
Ecology - methods
energy
Energy conservation
Energy consumption
Energy efficiency
Energy management
Energy use
Facility Design and Construction - methods
Facility Design and Construction - statistics & numerical data
Greenhouse Effect - prevention & control
Greenhouse Effect - statistics & numerical data
Greenhouse gases
Humans
Mathematical analysis
Mathematical Concepts
Measuring instruments
Meters
Statistics as Topic - methods
analytics
performance
data
buildings
energy
ISSN:0077-8923, 1749-6632, 1749-6632
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Abstract Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios.
AbstractList Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios.Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios.
Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios. [PUBLICATION ABSTRACT]
Many buildings are now collecting a large amount of data on operations, energy consumption, and activities through systems such as a building management system (BMS), sensors, and meters (e.g., submeters and smart meters). However, the majority of data are not utilized and are thrown away. Science and mathematics can play an important role in utilizing these big data and accurately assessing how energy is consumed in buildings and what can be done to save energy, make buildings energy efficient, and reduce greenhouse gas (GHG) emissions. This paper discusses an analytical tool that has been developed to assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, and simulating energy consumption in building portfolios.
Author Horesh, Raya
An, Lianjun
Zhang, Rui
Liu, Fei
Lee, Young M.
Chae, Young Tae
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Cites_doi 10.1088/0957-0233/9/6/001
10.1016/j.enbuild.2008.06.013
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Keywords analytics
performance
data
buildings
energy
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SubjectTerms Air Pollution - prevention & control
Air Pollution - statistics & numerical data
analytics
Building management systems
Buildings
data
Ecology - methods
energy
Energy conservation
Energy consumption
Energy efficiency
Energy management
Energy use
Facility Design and Construction - methods
Facility Design and Construction - statistics & numerical data
Greenhouse Effect - prevention & control
Greenhouse Effect - statistics & numerical data
Greenhouse gases
Humans
Mathematical analysis
Mathematical Concepts
Measuring instruments
Meters
Statistics as Topic - methods
Title Applying science and mathematics to big data for smarter buildings
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