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Tagging with AI to Enable
Data-Driven Applications at Scale
Jason Koh, PhD
Chief Data Scientist at Mapped
1
2
3
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
4
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
5
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
6
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
7
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
8
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
9
Cloud hosted via
site-to-site VPN
Discover &
Extract
Or
Or
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
10
Technical Challenges in Auto-Mapping
Heterogeneous
Buildings
Complex
Relationships
Expensive
Labeling Effort
Lack of
Labeled Data
11
Active
Learning
Transfer
Learning
Ensemble
Learning
Mapped Approach
A New Building
Known Buildings
Machine Learning
Ensemble DAG
Domain Experts
Mapped Graph
12
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
13
2. Infer unlabeled data with the model
3. Ask labels for less confident inference
4. Repeat
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
14
Machine Learning
Models
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
15
Mapped Approach
A New Building
Known Buildings
Machine Learning
Ensemble DAG
Domain Experts
Mapped Graph
16
Active
Learning
Transfer
Learning
Ensemble
Learning
Mapped in a Slide
17
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
Jason Koh jason@mapped.com
18
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
19
© 2021 MAPPED. ALL RIGHTS RESERVED 20
Scrabble: Transferable Active Learning [Koh et al. 2018]
R M 3 - Z N T
Sentence
BIO Tags
Conditional
Random
Fields (CRF)
Robust to
variations
Room
id ⦰
Zone
Temp
Tags
(IR)
Entity Class Room ZoneTempSensor
Multi-Label
MLP
common layer
across buildings
RM-3 RM3-ZNT
Mapped
Graph
hasLocation
Inference
from Schema
static rules
20

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Tagging with AI to Enable Data-Driven Smart Building Applications at Scale!

  • 1. Tagging with AI to Enable Data-Driven Applications at Scale Jason Koh, PhD Chief Data Scientist at Mapped 1
  • 2. 2
  • 3. 3
  • 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 4
  • 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 5
  • 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 6
  • 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 7
  • 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 8
  • 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 9 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 10
  • 11. Technical Challenges in Auto-Mapping Heterogeneous Buildings Complex Relationships Expensive Labeling Effort Lack of Labeled Data 11 Active Learning Transfer Learning Ensemble Learning
  • 12. Mapped Approach A New Building Known Buildings Machine Learning Ensemble DAG Domain Experts Mapped Graph 12
  • 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 13 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 14 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 15
  • 16. Mapped Approach A New Building Known Buildings Machine Learning Ensemble DAG Domain Experts Mapped Graph 16 Active Learning Transfer Learning Ensemble Learning
  • 17. Mapped in a Slide 17 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 19
  • 20. © 2021 MAPPED. ALL RIGHTS RESERVED 20 Scrabble: Transferable Active Learning [Koh et al. 2018] R M 3 - Z N T Sentence BIO Tags Conditional Random Fields (CRF) Robust to variations Room id ⦰ Zone Temp Tags (IR) Entity Class Room ZoneTempSensor Multi-Label MLP common layer across buildings RM-3 RM3-ZNT Mapped Graph hasLocation Inference from Schema static rules 20