Memoori was joined by Jason Koh & Shaun Cooley from Mapped to discuss how they are using Artificial Intelligence to help existing buildings, which are often not well-aligned to Smart Building Ontologies, develop data-driven applications. We will also featured some findings from our recent AI research report – https://memoori.com/portfolio/ai-machine-learning-in-smart-commercial-buildings/
4. Potential of Building Data
Air Quality Estimation
Occupancy-driven
Energy Optimization
Customizable
Thermal Comfort
On-demand space booking
Automated Fault Detection
HVAC
Electricity
Security
Lightning
Elevator
Water
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5. Mapped, the Unified API
Air Quality Estimation
Occupancy-driven
Energy Optimization
Customizable
Thermal Comfort
On-demand space booking
Automated Fault Detection
HVAC
Electricity
Security
Lightning
Elevator
Water
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6. State of Building Data
- Open up your building management/automation system, look at the point names
- 3 different buildings/BMS/subsystems → 3 (or more) different labeling/naming schemes
Slide source: Gabe Fierro
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7. Mapped Ontology Extending Brick
Point
Equipment Location
feeds,
hasPart isPartOf
feeds
hasPoint
hasPoint
controls
- Introduce People on top of Brick
- Extend more Classes
(e.g., security, conference)
- Identities management
- Community-driven open-source
- Rigorously validated
- Extensible and expressive
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8. An Example Building Graph
AHU-1
VAV-101 VAV-102 Zone-102
Room-102-1 Room-102-2
ZNT101 SAF101
feeds
hasPoint
feeds
hasPart
Variable Air
Volume Box
Variable Air
Volume Box
Air Handling
Unit
type
HVAC Zone
Zone Air Temp
Sensor
Supply Air Flow
Sensor Room Room
HVAC & Structural Data
Jane Doe
Access Card
Reader 1
Security & People Data
Person
Jane’s
Corp Badge
Badge
hasLocation
Access
History 1
owns
hasObserved
hasPoint
Access History
Status
Access Card
Reader
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9. Unified Access
via GraphQL
{
building(id:"175A7C19") {
floors(level: "3") {
spaces {
id
name
geoshape
points {
type
timestamp
aggregate
}
}
}
}
}
Visualize &
Control Data
Data Flow
Fine-grained
Control
Mapped Data Pipeline
Mapping
with AI
Data Experts
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Cloud hosted via
site-to-site VPN
Discover &
Extract
Or
Or
10. The Metadata Mapping Problem
RM-101.VMA-101.ZN_T
type
HVAC_Zone VAV
type
feeds
AHU-1
RM-101.VMA-101.OCC
Zone Air
Temperature
Sensor
type
hasPoint
feeds
hasPoint
Occupancy
Command
type
AHU
type
1. Finding Entities / Types
2. Finding Relationships
Various AI Methods
- Point name analysis
- Timeseries clustering
- Causality / correlation learning
Goals
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11. Technical Challenges in Auto-Mapping
Heterogeneous
Buildings
Complex
Relationships
Expensive
Labeling Effort
Lack of
Labeled Data
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Active
Learning
Transfer
Learning
Ensemble
Learning
12. Mapped Approach
A New Building
Known Buildings
Machine Learning
Ensemble DAG
Domain Experts
Mapped Graph
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13. Active Learning
1. Train a model with given labeled data
Machine Learning
Models
Domain
Experts
What is the most efficient way of
acquiring labels?
1.
2.
3.
At Mapped:
1. Various statistical metrics
2. Highly optimized labeling tool
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2. Infer unlabeled data with the model
3. Ask labels for less confident inference
4. Repeat
14. Transfer Learning
1. Shift data distribution
2. Cherry-pick common features
3. Ask uncommon patterns to
domain experts
A New
Building
Known
Buildings
How to utilize
different buildings’ data?
At Mapped:
1. Domain adaptation models
2. Similarity analysis for co-training
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Machine Learning
Models
15. Ensemble Learning
1. Voting, decision tree, boosting, etc.
2. Directed Acyclic Graph (DAG) for
complementing different ML models
Machine Learning
Ensemble DAG
How to ensemble various ML models?
At Mapped:
1. Flexible DAG based on customer requirements
2. Learned priorities over different ML models
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16. Mapped Approach
A New Building
Known Buildings
Machine Learning
Ensemble DAG
Domain Experts
Mapped Graph
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Active
Learning
Transfer
Learning
Ensemble
Learning
17. Mapped in a Slide
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Mapped solves the complicated ML problem with
active, transfer, and ensemble learning
Applications need a single API with
a common view of the world
The automated data pipeline from discovery to
mapping, control, and usage.
Air Quality Estimation
Occupancy-driven
Energy Optimization
On-demand space
booking
HVAC
Electricity
Security
Lightning
19. Some Numbers from Mapped
1. Scrabble++
a. 97% accuracy with 1-2 % labeled data for a new building
b. 20% increased accuracy with transfer learning (when there are not many examples)
c. 30 seconds to manually label an entity (or a BACnet object)
2. Equipment correlation learning
a. 93% accuracy without labels but with ground-truth point labels
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