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