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市場調査レポート

インテリジェント・ビルとビッグデータ

Intelligent Buildings and Big Data

発行 CABA (Continental Automated Buildings Association) 商品コード 339684
出版日 ページ情報 英文
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インテリジェント・ビルとビッグデータ Intelligent Buildings and Big Data
出版日: 2015年02月01日 ページ情報: 英文
概要

当レポートでは、インテリジェント・ビルとビッグデータについて調査分析を行い、ビッグデータの概要、インテリジェント・ビルにおけるビッグデータの例、今後の動向などについてまとめています。

第1章 エグゼクティブサマリー

第2章 ビッグデータの概要

  • 本レポートについて
  • スポンサー
  • 運営委員会の役割
  • CABAについて
  • Navigant Researchについて
  • イントロダクション
  • ビッグデータの定義
  • 分析プロセス
  • ビルのデータフロー
  • ビッグデータ分析
  • ビッグデータの例

第3章 インテリジェント・ビルにおけるビッグデータの例

  • イントロダクション
  • ビッグデータとインテリジェント・ビルの運用
  • ビッグデータとインテリジェント・ビルのモノのインターネット(IoT)
  • ビルシステム
  • データ統合
  • ビルへのビッグデータ導入の影響
  • ビッグデータソリューションの提供
  • ビッグデータの課題

第4章 CABA建物データ参考資料

  • 概要
  • 主要ビル活動
  • 規模
  • データ量と速度
  • 結果

第5章 インテリジェント・ビルにおけるビッグデータの認知

  • 概要
  • 調査方法
  • 調査結果

第6章 ビッグデータ・ソリューション

第7章 ビッグデータ市場ソリューションの動向

第8章 結論

  • 概要
  • インテリジェント・ビルにおけるビッグデータの現在の市場
  • インテリジェント・ビルにおけるビッグデータの機会

第9章 略語リスト

第10章 付録A

第11章 付録B

図表

目次

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

Section 3: Executive Summary

  • 3.1 About this Report
  • 3.2 Sponsors
  • 3.3 Role of the Steering Committee
  • 3.4 About CABA
  • 3.5 About Navigant Research
  • 3.6 Introduction
  • 3.7 The Challenge of Big Data in Intelligent Buildings
    • 3.7.1 Defining Big Data in Intelligent Buildings
    • 3.7.2 Business Case
    • 3.7.3 Market Maturity
      • 3.7.3.1 The Big Data Reference
  • 3.8 Research Approach
    • 3.8.1 Methodology
  • 3.9 Major Findings
  • 3.10 Overview of Report Content
  • 3.11 Case Studies
    • 3.11.1 Vancouver Coastal Health Authority Case Study - Eco Opera
      • 3.11.1.1 Key Highlights
      • 3.11.1.2 Features
      • 3.11.1.3 Project Overview
      • 3.11.1.4 Facility Details
      • 3.11.1.5 EcoCEO Solution from Eco Opera Systems Inc
      • 3.11.1.6 Project Stages
      • 3.11.1.7 Whole Building Level Analysis (Eco Track)
      • 3.11.1.8 Systems Level Analysis (EcoLEED M&V and ExoOptimizer)
      • 3.11.1.9 Key Achievements
      • 3.11.1.10 Performance Optimiztaion Opportunities
    • 3.11.2 Seneca Manufacturing Facility - Schneider Electric
      • 3.11.2.1 Key Highlights
      • 3.11.2.2 Features
      • 3.11.2.3 Project Overview
      • 3.11.2.4 Facility Details
      • 3.11.2.5 Building Analytics Service from Schneider Electric
      • 3.11.2.6 Project Stages
      • 3.11.2.7 Key Achievements

Section 4: Overview On Big Data

  • 4.1 About this Report
  • 4.2 Sponsors
  • 4.3 Role of the Steering Committee
  • 4.4 About CABA
  • 4.5 About Navigant Research
  • 4.6 Introduction
  • 4.7 Defining Big Data
  • 4.8 Analytics Process
  • 4.9 Building Data Flow
  • 4.10 Big Data Analytics
  • 4.11 Big Data Examples
    • 4.11.1 Financial Services
    • 4.11.2 Government
    • 4.11.3 Marketing
    • 4.11.4 Meteorology
    • 4.11.5 Retail

Section 5: The Case For Big Data In Intelligent Buildings

  • 5.1 Introduction
  • 5.2 Big Data and Intelligent Buildings Operations
    • 5.2.1 The Role of Big Data in Buildings Operations
    • 5.2.2 The Role of Big Data in Energy Efficiency
    • 5.2.3 Metrics Used for Investment
  • 5.3 Big Data and Intelligent Buildings Internet of Things (IoT)
  • 5.4 Building Systems
    • 5.4.1 Energy
    • 5.4.2 HVAC
    • 5.4.3 Lighting
    • 5.4.4 Security and Access Control
    • 5.4.5 External Data
    • 5.4.6 Ancillary Systems
  • 5.5 Data Integration
    • 5.5.1 Building Communication Protocols
      • 5.5.1.1 BACnet
      • 5.5.1.2 LonWorks
      • 5.5.1.3 KNX
      • 5.5.1.4 BatiBUS ran(France)
      • 5.5.1.5 EIB
      • 5.5.1.6 DALI
      • 5.5.1.7 Modbus
      • 5.5.1.8 oBIX
    • 5.5.2 Big Data Systems
    • 5.5.3 Data Volume and Velocity
  • 5.6 Influences on the Application of Big Data in Buildings
    • 5.6.1 Building Use
      • 5.6.1.1 Office
      • 5.6.1.2 Retail
    • 5.6.2 Property Portfolio
  • 5.7 Big Data Solution Offerings
    • 5.7.1 Visualization and Reporting
    • 5.7.2 Fault Detection and Diagnostics
    • 5.7.3 Predictive Maintenance and Continuous Improvement
    • 5.7.4 Optimization
  • 5.8 Big Data Challenges
    • 5.8.1 Privacy
    • 5.8.2 Data Security

Section 6: The Caba Building Data Reference

  • 6.1 Overview
  • 6.2 Primary Building Activity
  • 6.3 Size
  • 6.4 Data Volume and Velocity
  • 6.5 Results

Section 7: The Perception Of Big Data In Intelligent Buildings

  • 7.1 Overview
  • 7.2 Methodology
    • 7.2.1 Interviews
    • 7.2.2 Survey
  • 7.3 Findings
    • 7.3.1 Interview Findings
    • 7.3.2 Survey Findings

Section 8: Big Data Solutions

  • 8.1 Overview
  • 8.2 Sector- and Entry-Point Solutions and Pathways
    • 8.2.1 Retail
    • 8.2.2 Enterprise/Office
  • 8.3 Solution Offerings
  • 8.4 Differing Perspectives on the Market
    • 8.4.1 Market Dynamics
  • 8.5 Education and Training of Building Operators and Managers
  • 8.6 Value Propositions for Big Data Adoption
  • 8.7 Value Propositions for Vendors
  • 8.8 Innovative Best Practices

Section 9: Trends In Big Data Market Solutions

  • 9.1 Forecast Summary
  • 9.2 Methodology
  • 9.3 Offering Types
  • 9.4 Customer Types
    • 9.4.1 Enterprise/Office
    • 9.4.2 Retail
  • 9.5 Big Data in Intelligent Buildings Forecast for North America
  • 9.6 Sectors

Section 10: Conclusions

  • 10.1 Overview
  • 10.2 Current Market for Big Data in Intelligent Buildings
    • 10.2.1 Challenges and Recommendations
  • 10.3 The Opportunity for Big Data in Intelligent Buildings

Section 11: Acronym And Abbrevlation List

Section 12: Appendix A: Big Data Survey Questionnaire

Section 13: Appendix B: Big Data Survey Results

CHARTS

  • 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)
  • 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 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 Systems Schematic
  • Figure 3-14 Relative Energy Load for St. Mary's Hospital
  • Figure 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 CAGA Building Data Reference
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