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Intelligent Buildings and Big Data

  • January 2015
  • -
  • Continental Automated Building Association (CABA)
  • -
  • 127 pages

The Continental Automated Buildings Association (CABA) commissioned Navigant Research to study new tools and
resources emerging in the market to help companies filter, analyze, and use Big Data collected from their intelligent and integrated buildings. Leveraging Big Data will enable a better understanding of customer behaviors, competition, and market trends. Research on utilizing Big Data from building systems is crucial to staying competitive in this dynamic connected marketplace.

Navigant Research and the Steering Committee first convened via a webinar in July 2014, and established a regular schedule of discussion and collaboration for the duration of the project. The findings presented in this report showcase the results of primary and secondary research including in-depth executive interviews and a broad stakeholder online survey.

The outcomes of this collaborative research project will provide a clear understanding of the opportunities and solutions of managing data derived from intelligent buildings. This research examined how data from intelligent buildings can be more efficiently filtered, analyzed, and ultimately used by all segments of the industry. This information will eventually lead to greater productivity, reliability, efficiency, and operational control of intelligent buildings.

The Continental Automated Buildings Association (CABA) commissioned Navigant Research to study new tools and resources emerging in the market to help companies filter, analyze, and use Big Data collected from their intelligent and integrated buildings. Leveraging Big Data will enable a better understanding of customer behaviors, competition, and market trends. Research on utilizing Big Data from building systems is crucial to staying competitive in this dynamic connected marketplace.

Navigant Research and the Steering Committee first convened via a webinar in July 2014, and established a regular schedule of discussion and collaboration for the duration of the project. The findings presented in this report showcase the results of primary and secondary research including in-depth executive interviews and a broad stakeholder online survey.

The outcomes of this collaborative research project will provide a clear understanding of the opportunities and solutions of managing data derived from intelligent buildings. This research examined how data from intelligent buildings can be more efficiently filtered, analyzed, and ultimately used by all segments of the industry. This information will eventually lead to greater productivity, reliability, efficiency, and operational control of intelligent buildings.

Table Of Contents

Intelligent Buildings and Big Data
Section 3: Executive Summary
About this Report
Sponsors
Role of the Steering Committee
About CABA
About Navigant Research
Introduction
The Challenge of Big Data in Intelligent Buildings
Defining Big Data in Intelligent Buildings
Business Case
Market Maturity
3.7.3.1 The Big Data Reference
Research Approach
Methodology
Major Findings
Overview of Report Content
Case Studies
Key Highlights
Features
Project Overview
Facility Details
EcoCEO Solution from Eco Opera Systems Inc.
Project Stages
Whole Building Level Analysis (EcoTrack)
Systems Level Analysis (EcoLEED MandV and EcoOptimizer)
Key Achievements
Performance Optimization Opportunities
3.11.2.3 Project Overview
Building Analytics Service from Schneider Electric
Project Stages
Section 4: Overview On Big Data
About this Report
Sponsors
Role of the Steering Committee
About CABA
About Navigant Research
Introduction
Defining Big Data
Analytics Process
Building Data Flow
Big Data Analytics
Big Data Examples
Financial Services
Government
Marketing
Meteorology
Retail
Section 5: The Case For Big Data In Intelligent Buildings
Introduction
Big Data and Intelligent Buildings Operations
The Role of Big Data in Energy Efficiency
Metrics Used for Investment
Big Data and Intelligent Buildings Internet of Things (IoT)
Building Systems
Energy
HVAC
Lighting
Security and Access Controls
External Data
Ancillary Systems
Data Integration
Building Communication Protocols
BACnet
LonWorks
KNX
BatiBUS (France)
EIB
DALI
Modbus
oBIX
Big Data Systems
Data Volume and Velocity
Influences on the Application of Big Data in Buildings
Building Use
Office
Retail
Property Portfolios
Big Data Solution Offerings
Visualization and Reporting
Fault Detection and Diagnostics
Predictive Maintenance and Continuous Improvement
Optimization
Big Data Challenges
Privacy
Data Security
Section 6: The Caba Building Data Reference
Overview
Primary Building Activity
Size
Data Volume and Velocity
Results
Section 7: The Perception Of Big Data In Intelligent Buildings
Overview
Methodology
Interviews
Survey
Findings
Interview Findings
Survey Findings
Section 8: Big Data Solutions
Overview
Sector- and Entry-Point Solutions and Pathways
Retail
Enterprise/Office
Solution Offerings
Differing Perspectives on the Market
Market Dynamics
Education and Training of Building Operators and Managers
Value Propositions for Big Data Adoption
Value Propositions for Vendors
Innovative Best Practices
Section 9: Trends In Big Data Market Solutions
Forecast Summary
Methodology
Offering Types
Customer Types
Enterprise/Office
Retail
Big Data in Intelligent Buildings Forecast for North America
Sectors
Section 10: Conclusions
Overview
Current Market for Big Data in Intelligent Buildings
10.2.1 Challenges and Recommendations
The Opportunity for Big Data in Intelligent Buildings
Section 11: Acronym And Abbreviation List
Section 12: Appendix A: Big Data Survey Questionnaire
Section 13: Appendix B: Big Data Survey Results
SECTION 2: TABLE OF CHARTS, FIGURES AND TABLES
Chart 1.1 On a scale of 1 to 5, where 1 is not knowledgeable at all and 5 is extremely knowledgeable, how do you rate your knowledge about the concept of big data and the application of big data to buildings? (n=400)
Chart 5.2 On a scale of 1 to 5 where 1 is not important at all and 5 is extremely important, please rate how important the following factors are when making improvements to your building. (n=400)
Chart 3.3 On a scale of 1 to 5, where 1 is not concerned at all and 5 is extremely concerned, how concerned are you about the following issues as it relates to data collected in your building? (n=400)
Chart 1.2 Big Data in Intelligent Buildings Revenue, North America: 2015-2020
Chart 3.12 Hourly Electrical Demand Heat Map of Reporting Period
Chart 3.13 Energy Use in St. Mary's Hospital
Chart 4.1 Total Number of Control Points by Building Size and Principal Building Activity
Chart 4.2 Average Data Transactions per Day for Small Retail Buildings by Data Volume
Chart 4.3 Average Data Transactions per Day for Large Retail Buildings by Data Volume
Chart 4.4 Average Data Transactions per Day for Small Office Buildings by Data Volume
Chart 4.5 Average Data Transactions per Day for Large Office Buildings by Data Volume
Chart 5.1 Interviews Completed by Category (n=34) 62
Chart 5.2 On a scale of 1 to 5 where 1 is not important at all and 5 is extremely important, please rate how important the following factors are when making improvements to your building. (n=400)
Chart 5.3 Which of the following describes your level of familiarity with analytics in relation to building management? (n=400)
Chart 5.4 On a scale of 1 to 5, where 1 is not knowledgeable at all and 5 is extremely knowledgeable, how do you rate your knowledge about the concept of big data and the application of big data to buildings? (n=400))
Chart 5.5 Which of the following do you think best describes big data in buildings? (n=400)
Chart 5.6 On a scale of 1 to 5, where 1 is not concerned at all and 5 is extremely concerned, how concerned are you about the following issues as it relates to data collected in your building? (n=400)
Chart 5.7 Please rank the following design characteristics or functionality of an energy management system on a scale of 1 to 5 where 1 is not at all valuable and 5 is extremely valuable. (n=400)
Chart 5.8 If you had $100 to spend on this energy management system, how much would you spend on each individual feature? (n=400)
Chart 5.9 How often would you like this energy management system to provide reports? (n=400)
Chart 5.10 On a scale of 1 to 5 where 1 is not at all interested and 5 is extremely interested, how interested would you be in installing this system in your building? (n=400)
Chart 5.11 Job Function of Respondent by Interest in Energy Management Systems
Chart 5.12 Desired Frequency of Energy Management Reports by Interest in Energy Management Systems
Chart 5.13 Use of Cloud Services by Interest in Energy Management Systems
Chart 5.14 Average Rating of Knowledge about Big Data by Interest in Energy Management Systems
Chart 5.15 Primary Building Activity by Interest in Energy Management Systems
Chart 5.16 Building Size by Interest in Energy Management Systems
Chart 5.17 Annual Electricity Spending by Interest in Energy Management Systems
Chart 5.18 Operating Expense Budget by Interest in Energy Management Systems
Chart 5.19 Capital Expense Budget by Interest in Energy Management Systems
Chart 5.20 Average Rating of Importance of Factors when Making Building Improvements by Interest in Energy Management Systems
Chart 5.21 Average Concern about Issues Relating to Data Collected in a Building by Interest in Energy Management Systems
Chart 5.22 Average Rating of Data Analysis Skills of Building Management Personnel by Interest in Energy Management Systems
Chart 5.23 Average Rating of Willingness to Accept New Technology by Building Management Personnel by Interest in Energy Management Systems
Chart 5.24 Average Ranking of Design Characteristics or Functionality of Energy Management Systems by Interest in Energy Management Systems
Chart 5.25 Average Comfort Rating for Cloud Services by Interest in Energy Management Systems
Chart 5.26 Average Interest in Combining Data with Data from Other Buildings to Provide Analytics by Interest in Energy Management Systems
Chart 7.1 Big Data in Intelligent Buildings Revenue by Offering Type, North America: 2015-2020
Chart 7.2 Big Data in Intelligent Buildings Revenue by Segment, Select Segments, North America: 2015-2020
Chart 13.1 What country are you located in?(n=400)
Chart 13.2 Please select the level of influence you have in purchasing the following products and services for your company or organization. (n=400)
Chart 13.3 Which best describes your function at your company or organization? (n=400)
Chart 13.4 On a scale of 1 to 5 where 1 is not important at all and 5 is extremely important, please rate how important the following factors are when making improvements to your building. (n=400)
Chart 13.5 On a scale of 1 to 5, where 1 is not knowledgeable at all and 5 is extremely knowledgeable, how do you rate your knowledge about the concept of big data and the application of big data to buildings? (n=400)
Chart 13.6 Which of the following do you think best describes big data in buildings? (n=400)
Chart 13.7 Which of the following describes your level of familiarity with analytics in relation to building management? (n=400)
Chart 13.8 On a scale of 1 to 5, where 1 is does not use data analysis to make decisions at all and 5 is uses data analysis to make every decision, how much does your company or organization rely on data analysis for general business operations? (n=400)
Chart 13.9 On a scale of 1 to 5, where 1 is not concerned at all and 5 is extremely concerned, how concerned are you about the following issues as it relates to data collected in your building? (n=400)
Chart 13.10 On a scale of 1 to 5, where 1 is no skills at all and 5 is extremely skilled, how do you rate the skills of the people at your company responsible for building management in understanding data analysis? (n=400)
Chart 13.11 On a scale of 1 to 5, where 1 is not willing at all and 5 is extremely willing, how do you rate the willingness of the people at your company responsible for building management workforce to accept new technology? (n=400)
Chart 13.12 Does your company or organization currently analyze the electricity consumption of devices in the buildings you operate? (n=400)
Chart 13.13 What is the most granular level you analyze electricity consumption on? (n=198)
Chart 13.14 What data sources does your company us to analyze the electricity consumption of devices in your building? Please select all that apply. (n=198)
Chart 13.15 How do you analyze electricity consumption? Please select all that apply. (n=198)
Chart 13.16 How often do you use information from your energy management system to configure the equipment in your facilities, such as by changing setpoints and schedules? (n=198)
Chart 13.17 What reasons does your company or organization have for not using an energy management system? Select all that apply. (n=167)
Chart 13.18 Are there metering or sensor devices installed in your building to measure the energy consumption of the following systems? (n=49)
Chart 13.19 Are there metering or sensor devices installed in your building to measure the energy consumption of the following systems? (n=49)
Chart 13.20 Approximately how many of each of the following devices do you currently manage the energy consumption of in your building?
Chart 13.21 Over what time interval is the data in your energy management system logged? (n=49)
Chart 13.22 On a scale of 1 to 5 where 1 is not at all interested and 5 is extremely interested, how interested would you be in installing this system in your building? (n=400)
Chart 13.23 Please rank the following design characteristics or functionality of an energy management system on a scale of 1 to 5 where 1 is not at all valuable and 5 is extremely valuable. (n=400)
Chart 13.24 How often would you like this energy management system to provide reports? (n=400)
Chart 13.25 If you had $100 to spend on this energy management system, how would you spend on each individual feature? (n=400)
Chart 13.26 On a scale of 1 to 5, where 1 is not comfortable at all and 5 is extremely comfortable, how would comfortable are you:
Chart 13.27 Are you more comfortable with data being stored on a public cloud (one managed by a third party like HP, Amazon, or Oracle) or a corporate cloud that is managed by your own company? (n=400)
Chart 13.28 Does your company currently use cloud services such as Salesforce, Google Docs, Carbonite, or Dropbox? (n=400)
Chart 13.29 What cloud services does your company use? Please select all that apply. (n=187)
Chart 13.30 How interested would your company or organization be in sharing your building data with a company that could combine this data with data from other buildings to provide analytics? (n=400)
Chart 13.31 Which of the following energy-related tasks are you responsible for in your role at your company or organization? Please select all that apply. (n=400)
Chart 13.32 What is the primary building activity of the building that houses your company or organization? (n=400)
Chart 13.33 Approximately how large is the building that houses your company or organization? (n=400)
Chart 13.34 Does your company or organization operate in just one building or does it have a portfolio of buildings? (n=400)
Chart 13.35 Are you involved in purchasing building automation systems or energy management systems for just one building or more than one building? (n=169)
Chart 13.36 How much does your company or organization typically spend on electricity in one year? (n=400)
Chart 13.37 What is your company or organizations annual budget for building operating expenses (such as electricity use and maintenance) (n=400)
Chart 13.38 What is your company or organizations annual budget for building capital expenses (such as new equipment, upgrades, or retrofits) (n=400)
FIGURES
Figure 3-1 Generic Building Data Flow
Figure 3-2 Big Data Solution Offerings for Intelligent Buildings
Figure 3-3 Example Analytics Process for Lighting Applications
Figure 3-4 Customer Value Propositions for Big Data Solution Offerings
Figure 3-5 Big Data Solutions and the Convergence of Facilities, Business, and Energy Management
Figure 3-6 Market Dynamics for Big Data in Intelligent Buildings
Figure 3-8 Eco Opera Systems at Vancouver Coastal Health Authority
Figure 3-9 EcoCEO Components
Figure 3-10 Eco Opera Systems
Figure 3-11 Eco Opera System Schematic
Figure 3-14 Relative Energy Load for St. Mary's HospitalFigure 3-15 St. Mary's Hospital Hydronic System Diagram
Figure 3-16 Schneider Electric's Seneca Manufacturing Facility
Figure 3-17 Building Analytics Design Features
Figure 4-1 Analytics Process
Figure 4-2 Generic Building Data Flow
Figure 5-1 Example Analytics Process for HVAC Applications
Figure 5-2 Example Analytics Process for Lighting Applications
Figure 5-3 BACnet Collapsed Architecture
Figure 5 4 Modbus Protocol Stack
Figure 8-1 Evolving Landscape of Business Intelligence Tools
Figure 8-2 Big Data Solution Offerings for Intelligent Buildings
Figure 8-3 Big Data Solutions and the Convergence of Facilities, Business, and Energy Management
Figure 8-4 Varying Perspectives on Big Data Solution Offerings
Figure 8-5 Market Dynamics for Big Data Solutions in Intelligent Buildings
Figure 8-6 Customer Needs and Vendor Opportunities
Figure 8-7 Customer Value Propositions for Big Data Solution Offerings
Figure 8-8 SWOT Assessment of Big Data Market Opportunity for Vendors

TABLES

Table 4.1 Summary of the CABA Building Data Reference
Section 3: Executive Summary
About this Report
Sponsors
Role of the Steering Committee
About CABA
About Navigant Research
Introduction
The Challenge of Big Data in Intelligent Buildings
Defining Big Data in Intelligent Buildings
Business Case
Market Maturity
3.7.3.1 The Big Data Reference
Research Approach
Methodology
Major Findings
Overview of Report Content
Case Studies
Key Highlights
Features
Project Overview
Facility Details
EcoCEO Solution from Eco Opera Systems Inc.
Project Stages
Whole Building Level Analysis (EcoTrack)
Systems Level Analysis (EcoLEED MandV and EcoOptimizer)
Key Achievements
Performance Optimization Opportunities
3.11.2.3 Project Overview
Building Analytics Service from Schneider Electric
Project Stages
Section 4: Overview On Big Data
About this Report
Sponsors
Role of the Steering Committee
About CABA
About Navigant Research
Introduction
Defining Big Data
Analytics Process
Building Data Flow
Big Data Analytics
Big Data Examples
Financial Services
Government
Marketing
Meteorology
Retail
Section 5: The Case For Big Data In Intelligent Buildings
Introduction
Big Data and Intelligent Buildings Operations
The Role of Big Data in Energy Efficiency
Metrics Used for Investment
Big Data and Intelligent Buildings Internet of Things (IoT)
Building Systems
Energy
HVAC
Lighting
Security and Access Controls
External Data
Ancillary Systems
Data Integration
Building Communication Protocols
BACnet
LonWorks
KNX
BatiBUS (France)
EIB
DALI
Modbus
oBIX
Big Data Systems
Data Volume and Velocity
Influences on the Application of Big Data in Buildings
Building Use
Office
Retail
Property Portfolios
Big Data Solution Offerings
Visualization and Reporting
Fault Detection and Diagnostics
Predictive Maintenance and Continuous Improvement
Optimization
Big Data Challenges
Privacy
Data Security
Section 6: The Caba Building Data Reference
Overview
Primary Building Activity
Size
Data Volume and Velocity
Results
Section 7: The Perception Of Big Data In Intelligent Buildings
Overview
Methodology
Interviews
Survey
Findings
Interview Findings
Survey Findings
Section 8: Big Data Solutions
Overview
Sector- and Entry-Point Solutions and Pathways
Retail
Enterprise/Office
Solution Offerings
Differing Perspectives on the Market
Market Dynamics
Education and Training of Building Operators and Managers
Value Propositions for Big Data Adoption
Value Propositions for Vendors
Innovative Best Practices
Section 9: Trends In Big Data Market Solutions
Forecast Summary
Methodology
Offering Types
Customer Types
Enterprise/Office
Retail
Big Data in Intelligent Buildings Forecast for North America
Sectors
Section 10: Conclusions
Overview
Current Market for Big Data in Intelligent Buildings
10.2.1 Challenges and Recommendations
The Opportunity for Big Data in Intelligent Buildings
Section 11: Acronym And Abbreviation List
Section 12: Appendix A: Big Data Survey Questionnaire
Section 13: Appendix B: Big Data Survey Results
SECTION 2: TABLE OF CHARTS, FIGURES AND TABLES
Chart 1.1 On a scale of 1 to 5, where 1 is not knowledgeable at all and 5 is extremely knowledgeable, how do you rate your knowledge about the concept of big data and the application of big data to buildings? (n=400)
Chart 5.2 On a scale of 1 to 5 where 1 is not important at all and 5 is extremely important, please rate how important the following factors are when making improvements to your building. (n=400)
Chart 3.3 On a scale of 1 to 5, where 1 is not concerned at all and 5 is extremely concerned, how concerned are you about the following issues as it relates to data collected in your building? (n=400)
Chart 1.2 Big Data in Intelligent Buildings Revenue, North America: 2015-2020
Chart 3.12 Hourly Electrical Demand Heat Map of Reporting Period
Chart 3.13 Energy Use in St. Mary's Hospital
Chart 4.1 Total Number of Control Points by Building Size and Principal Building Activity
Chart 4.2 Average Data Transactions per Day for Small Retail Buildings by Data Volume
Chart 4.3 Average Data Transactions per Day for Large Retail Buildings by Data Volume
Chart 4.4 Average Data Transactions per Day for Small Office Buildings by Data Volume
Chart 4.5 Average Data Transactions per Day for Large Office Buildings by Data Volume
Chart 5.1 Interviews Completed by Category (n=34) 62
Chart 5.2 On a scale of 1 to 5 where 1 is not important at all and 5 is extremely important, please rate how important the following factors are when making improvements to your building. (n=400)
Chart 5.3 Which of the following describes your level of familiarity with analytics in relation to building management? (n=400)
Chart 5.4 On a scale of 1 to 5, where 1 is not knowledgeable at all and 5 is extremely knowledgeable, how do you rate your knowledge about the concept of big data and the application of big data to buildings? (n=400))
Chart 5.5 Which of the following do you think best describes big data in buildings? (n=400)
Chart 5.6 On a scale of 1 to 5, where 1 is not concerned at all and 5 is extremely concerned, how concerned are you about the following issues as it relates to data collected in your building? (n=400)
Chart 5.7 Please rank the following design characteristics or functionality of an energy management system on a scale of 1 to 5 where 1 is not at all valuable and 5 is extremely valuable. (n=400)
Chart 5.8 If you had $100 to spend on this energy management system, how much would you spend on each individual feature? (n=400)
Chart 5.9 How often would you like this energy management system to provide reports? (n=400)
Chart 5.10 On a scale of 1 to 5 where 1 is not at all interested and 5 is extremely interested, how interested would you be in installing this system in your building? (n=400)
Chart 5.11 Job Function of Respondent by Interest in Energy Management Systems
Chart 5.12 Desired Frequency of Energy Management Reports by Interest in Energy Management Systems
Chart 5.13 Use of Cloud Services by Interest in Energy Management Systems
Chart 5.14 Average Rating of Knowledge about Big Data by Interest in Energy Management Systems
Chart 5.15 Primary Building Activity by Interest in Energy Management Systems
Chart 5.16 Building Size by Interest in Energy Management Systems
Chart 5.17 Annual Electricity Spending by Interest in Energy Management Systems
Chart 5.18 Operating Expense Budget by Interest in Energy Management Systems
Chart 5.19 Capital Expense Budget by Interest in Energy Management Systems
Chart 5.20 Average Rating of Importance of Factors when Making Building Improvements by Interest in Energy Management Systems
Chart 5.21 Average Concern about Issues Relating to Data Collected in a Building by Interest in Energy Management Systems
Chart 5.22 Average Rating of Data Analysis Skills of Building Management Personnel by Interest in Energy Management Systems
Chart 5.23 Average Rating of Willingness to Accept New Technology by Building Management Personnel by Interest in Energy Management Systems
Chart 5.24 Average Ranking of Design Characteristics or Functionality of Energy Management Systems by Interest in Energy Management Systems
Chart 5.25 Average Comfort Rating for Cloud Services by Interest in Energy Management Systems
Chart 5.26 Average Interest in Combining Data with Data from Other Buildings to Provide Analytics by Interest in Energy Management Systems
Chart 7.1 Big Data in Intelligent Buildings Revenue by Offering Type, North America: 2015-2020
Chart 7.2 Big Data in Intelligent Buildings Revenue by Segment, Select Segments, North America: 2015-2020
Chart 13.1 What country are you located in?(n=400)
Chart 13.2 Please select the level of influence you have in purchasing the following products and services for your company or organization. (n=400)
Chart 13.3 Which best describes your function at your company or organization? (n=400)
Chart 13.4 On a scale of 1 to 5 where 1 is not important at all and 5 is extremely important, please rate how important the following factors are when making improvements to your building. (n=400)
Chart 13.5 On a scale of 1 to 5, where 1 is not knowledgeable at all and 5 is extremely knowledgeable, how do you rate your knowledge about the concept of big data and the application of big data to buildings? (n=400)
Chart 13.6 Which of the following do you think best describes big data in buildings? (n=400)
Chart 13.7 Which of the following describes your level of familiarity with analytics in relation to building management? (n=400)
Chart 13.8 On a scale of 1 to 5, where 1 is does not use data analysis to make decisions at all and 5 is uses data analysis to make every decision, how much does your company or organization rely on data analysis for general business operations? (n=400)
Chart 13.9 On a scale of 1 to 5, where 1 is not concerned at all and 5 is extremely concerned, how concerned are you about the following issues as it relates to data collected in your building? (n=400)
Chart 13.10 On a scale of 1 to 5, where 1 is no skills at all and 5 is extremely skilled, how do you rate the skills of the people at your company responsible for building management in understanding data analysis? (n=400)
Chart 13.11 On a scale of 1 to 5, where 1 is not willing at all and 5 is extremely willing, how do you rate the willingness of the people at your company responsible for building management workforce to accept new technology? (n=400)
Chart 13.12 Does your company or organization currently analyze the electricity consumption of devices in the buildings you operate? (n=400)
Chart 13.13 What is the most granular level you analyze electricity consumption on? (n=198)
Chart 13.14 What data sources does your company us to analyze the electricity consumption of devices in your building? Please select all that apply. (n=198)
Chart 13.15 How do you analyze electricity consumption? Please select all that apply. (n=198)
Chart 13.16 How often do you use information from your energy management system to configure the equipment in your facilities, such as by changing setpoints and schedules? (n=198)
Chart 13.17 What reasons does your company or organization have for not using an energy management system? Select all that apply. (n=167)
Chart 13.18 Are there metering or sensor devices installed in your building to measure the energy consumption of the following systems? (n=49)
Chart 13.19 Are there metering or sensor devices installed in your building to measure the energy consumption of the following systems? (n=49)
Chart 13.20 Approximately how many of each of the following devices do you currently manage the energy consumption of in your building?
Chart 13.21 Over what time interval is the data in your energy management system logged? (n=49)
Chart 13.22 On a scale of 1 to 5 where 1 is not at all interested and 5 is extremely interested, how interested would you be in installing this system in your building? (n=400)
Chart 13.23 Please rank the following design characteristics or functionality of an energy management system on a scale of 1 to 5 where 1 is not at all valuable and 5 is extremely valuable. (n=400)
Chart 13.24 How often would you like this energy management system to provide reports? (n=400)
Chart 13.25 If you had $100 to spend on this energy management system, how would you spend on each individual feature? (n=400)
Chart 13.26 On a scale of 1 to 5, where 1 is not comfortable at all and 5 is extremely comfortable, how would comfortable are you:
Chart 13.27 Are you more comfortable with data being stored on a public cloud (one managed by a third party like HP, Amazon, or Oracle) or a corporate cloud that is managed by your own company? (n=400)
Chart 13.28 Does your company currently use cloud services such as Salesforce, Google Docs, Carbonite, or Dropbox? (n=400)
Chart 13.29 What cloud services does your company use? Please select all that apply. (n=187)
Chart 13.30 How interested would your company or organization be in sharing your building data with a company that could combine this data with data from other buildings to provide analytics? (n=400)
Chart 13.31 Which of the following energy-related tasks are you responsible for in your role at your company or organization? Please select all that apply. (n=400)
Chart 13.32 What is the primary building activity of the building that houses your company or organization? (n=400)
Chart 13.33 Approximately how large is the building that houses your company or organization? (n=400)
Chart 13.34 Does your company or organization operate in just one building or does it have a portfolio of buildings? (n=400)
Chart 13.35 Are you involved in purchasing building automation systems or energy management systems for just one building or more than one building? (n=169)
Chart 13.36 How much does your company or organization typically spend on electricity in one year? (n=400)
Chart 13.37 What is your company or organizations annual budget for building operating expenses (such as electricity use and maintenance) (n=400)
Chart 13.38 What is your company or organizations annual budget for building capital expenses (such as new equipment, upgrades, or retrofits) (n=400)
FIGURES
Figure 3-1 Generic Building Data Flow
Figure 3-2 Big Data Solution Offerings for Intelligent Buildings
Figure 3-3 Example Analytics Process for Lighting Applications
Figure 3-4 Customer Value Propositions for Big Data Solution Offerings
Figure 3-5 Big Data Solutions and the Convergence of Facilities, Business, and Energy Management
Figure 3-6 Market Dynamics for Big Data in Intelligent Buildings
Figure 3-8 Eco Opera Systems at Vancouver Coastal Health Authority
Figure 3-9 EcoCEO Components
Figure 3-10 Eco Opera Systems
Figure 3-11 Eco Opera System Schematic
Figure 3-14 Relative Energy Load for St. Mary's HospitalFigure 3-15 St. Mary's Hospital Hydronic System Diagram
Figure 3-16 Schneider Electric's Seneca Manufacturing Facility
Figure 3-17 Building Analytics Design Features
Figure 4-1 Analytics Process
Figure 4-2 Generic Building Data Flow
Figure 5-1 Example Analytics Process for HVAC Applications
Figure 5-2 Example Analytics Process for Lighting Applications
Figure 5-3 BACnet Collapsed Architecture
Figure 5 4 Modbus Protocol Stack
Figure 8-1 Evolving Landscape of Business Intelligence Tools
Figure 8-2 Big Data Solution Offerings for Intelligent Buildings
Figure 8-3 Big Data Solutions and the Convergence of Facilities, Business, and Energy Management
Figure 8-4 Varying Perspectives on Big Data Solution Offerings
Figure 8-5 Market Dynamics for Big Data Solutions in Intelligent Buildings
Figure 8-6 Customer Needs and Vendor Opportunities
Figure 8-7 Customer Value Propositions for Big Data Solution Offerings
Figure 8-8 SWOT Assessment of Big Data Market Opportunity for Vendors
TABLES
Table 4.1 Summary of the CABA Building Data Reference

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ref:plp2015

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