AI and Predictive Maintenance in Intelligent Buildings

1.1 What are Intelligent Buildings, and How Have They Evolved Over Time?
1.1.1 The Evolution of Intelligent Buildings
1.1.2 Introduction to Smart Systems
1.1.3 The Evolution of Maintenance Management Best Practices and Technologies
1.1.4 Building Technology Maturity Sets the Stage for Artificial Intelligence
1.2 Predictive Maintenance Needs of Buildings Differ Greatly by Building Type
1.3.1 Technological Innovations Set the Stage for Smart System Applications
1.3.2 Ecosystem Maturity Leading to More Complicated Business Models and Data Sharing Practices
1.3.3 Potential for Further Lockdowns Affects the Distribution of Demand Across Building Types
1.4 Building Types and Case Studies
1.4.1 Medical
1.4.2 Commercial
1.4.3 Retail and Hospitality
1.4.4 Mission Critical
1.4.5 Public Venues
1.4.6 Institutional

2.1 Overview of Artificial Intelligence and Machine Learning
2.1.1 The Intelligent Buildings Data Pipeline
2.1.2 The Artificial Intelligence in Buildings Player Ecosystem
2.2 Use Cases for AI in Intelligent Buildings
2.2.1 AI-Enabled Predictive Maintenance
2.2.2 Energy Analytics and Efficiency Optimization with AI
2.2.3 Optimizing and Automating Building Operations with AI
2.3 Enabling the AI-Powered Intelligent Building
2.3.1 Integrating AI with Building Automation Systems
2.3.2 Security and Data Privacy Must be Considered
2.3.3 AI’s Role in the Net-Zero Future

3.1 Tenant Satisfaction is Tied to How Well Buildings Manage Maintenance Requirements
3.1.1 Maintenance Guarantees as a Factor Affecting Satisfaction
3.1.2 Pain Points and Expectations Surrounding Building Maintenance
3.2 Building Operators Recognize the Need for Smarter Maintenance Management Practice
3.2.1 Operator Frustrations with Buildings Maintenance Practices
3.2.2 Promising Sensor and Data Collection Practices
3.2.3 Value Propositions that Move the Needle
3.3 Specific Fears Must First be Overcome
3.3.1 Operators are Worried About Impact on Employment
3.3.2 Tenant Fears Regarding AI Adoption
3.3.3 Supplier Strategies to Overcome AI Barriers and Catalyze the Market
3.4 Occupant and Operator Willingness to Pay Presents Opportunities for AI and PMIB Ecosystem Participants
3.4.1 Operator Willingness to Pay Increases with Operational Benefits
3.4.2 Tenant Willingness to Pay Allows Buildings to Offset Costs by Raising Rents
3.5 Eliminating Barriers to Adoption
3.5.1 Costs Drive Action
3.5.2 Systems Integration and Data Management are Costly Hindrances

4.1 Predictive Maintenance Solutions Introduction and Overview
4.1.1 Components and Capabilities of Available PMIB Solutions
4.1.2 Supplier Landscape and The Evolution of the PMIB Market
4.1.3 Functions and Features of a Predictive Analytics System
4.2 The Interaction Between Predictive Maintenance and the Components of an Intelligent Buildings.
4.2.1 Heating, Ventilation, and Air Conditioning Systems
4.2.2 Energy Management
4.2.3 Occupant Comfort Systems (Lighting, Shading)
4.2.4 Water Management
4.2.5 Network Infrastructure and Communications
4.2.6 Elevators and Escalators
4.2.7 Electrical Distribution Equipment, Uninterruptible Power Supply (UPS) Systems, And Failover/Disaster Recovery
4.2.8 Structural Integrity
4.3 Technology is Converging to Enable Predictive Maintenance
4.3.1 Automation
4.3.2 Secure Remote Access
4.3.3 Digital Twins
4.3.4 Edge Computing
4.3.5 Cybersecurity

5.1 Across the PMIB Value Chain, Players Need to Act Now
5.1.1 OEM Strategic Recommendations
5.1.2 Software Provider Strategic Recommendations
5.1.3 Services Provider Strategic Recommendations
5.1.4 Recommendations for Buildings Owners/Property Managers
5.2 Interoperability and Collaboration is the Catalyst
5.3 AI and PMIB Report Conclusion

APPENDIX A: DETAILED SURVEY DATA
Building Operators Survey
Building Occupants Survey
APPENDIX B: INTERVIEW PARTICIPANTS
APPENDIX C: SOURCED RESEARCH REFERENCES
APPENDIX D: GLOSSARY
APPENDIX E: REFERENCES

Figure 1.1 Simple, Compound, and Complex Applications
Figure 1.2 Smart Systems Design Framework
Figure 1.3 Evolution of Maintenance Approaches
Figure 1.4 The Evolution of Artificial Intelligence
Figure 1.5 Seven Technologies Powering AI and Predictive Maintenance
Figure 1.6 The Intelligent Building Ecosystem
Figure 1.7 Intelligent Buildings Segmentation
Figure 1.8 Distribution of Buildings in The United States and Canada
Figure 2.1. AI Applications in Buildings Vary in Complexity
Figure 2.2. Tools and Technologies Across the AI Pipeline
Figure 2.3. The AI Player Landscape is Diverse
Figure 2.4 Toronto Airport Predictive Maintenance Case Study
Figure 2.5 Example Data-Driven Buildings System Architecture
Figure 3.1 Survey Targeting
Figure 3.2 Predictive Maintenance Adoption
Figure 3.3 Factors Affecting the Decision to Move into a Building
Figure 3.4 Maintenance Guarantee Types
Figure 3.5 The Importance of Smooth Operation
Figure 3.6 Primary Pain Points with Maintenance Management
Figure 3.7 Interest in Predictive Maintenance
Figure 3.8 Building Operator Maintenance Management Pain Points
Figure 3.9 Device Data Density
Figure 3.10 Data Storage Mechanisms
Figure 3.11 Factors that Influence Purchase
Figure 3.12 Inhibitors to AI Adoption
Figure 3.13 Automation Preferences
Figure 3.14 Realized Benefits of Predictive Maintenance
Figure 3.15 Reservations Around AI
Figure 3.16 Barriers to Adoption
Figure 3.17 Operator Willingness to Pay for Predictive Maintenance
Figure 3.18 Occupant Willingness to Pay for Predictive Maintenance
Figure 3.19 Smart Systems Sales Funnel
Figure 4.1 Buildings Systems and Their Condition Monitoring Equipment
Figure 4.2 Predictive Maintenance: From Data to Deliverables
Figure 4.3 Microsoft HVAC & Lighting Predictive Maintenance Case Study
Figure 4.4 Nevada State Library and Archives Predictive Maintenance Case Study
Figure 4.5 American Discount Retailer Case Study
Figure 4.6 Vancouver International Airport Case Study
Figuer 4.7 Enterprise Data Center Case Study
Figure 4.8 Predictive Maintenance for Elevators and Escalators Case Study
Figure 4.9 Energy Savings at University of Iowa Using Schneider Electric's EcoStruxure Building Advisor
Figure 4.10 Illustrative Automation Process in Buildings.
Figure 4.11 Cloud Network Security Applications
Figure 4.12 Illustrative SASE and Supplier Partnerships
Figure 4.13 Information Flow in a Digital Twin
Figure 4.14 Digital Twin Levels of Complexity
Figure 4.15 Cybersecurity Providers
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