Creating a Healthcare Facility’s Digital Twin Through Big Data and the IoT

October 29, 2018

BuildingIQ Snapshot

• Building optimization company• Establishing the future of how AI interacts with buildings• Making smarter buildings that are more economical to operate

• 1100 buildings - 115+ million ft2 serviced with healthcare, commercial office, K-12, higher ed, government, museums, utilities, and enterprises in North American, Middle East, Australia, SE Asia

• Since 2009• ASX:BIQ since January 2016• ~45 people• Operations in United States, Australia, Philippines, and Singapore• 24/7 Network Operations Center

Strategic Partners

Industry Recognition

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Before We Get Into Digital Twins, a Brief Walk Down Memory Lane

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What in the world is this?

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What is a Digital Twin?

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Digital Twin – by Wikipedia

A digital replica of physical assets, processes and systems that can be used for various purposes. Provides both the elements and the dynamics of how an Internet of Things device operates and lives throughout its life cycle. Digital twins integrate artificial intelligence, machine learning and software analytics with data to create living digital simulation models that update and change as their physical counterparts change.

A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. This learning system, learns from itself, using sensor data that conveys various aspects of its operating condition; from human experts, such as engineers with deep and relevant industry domain knowledge; from other similar machines; from other similar fleets of machines; and from the larger systems and environment in which it may be a part of. A digital twin also integrates historical data…

A digital replica of physical assets, processes and systems that can be used for various purposes. Provides both the elements and the dynamics of how an Internet of Things device operates and lives throughout its life cycle. Digital twins integrate artificial intelligence, machine learning and software analytics with data to create living digital simulation models that update and change as their physical counterparts change.A digital twin continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position. This learning system, learns from itself, using sensor data that conveys various aspects of its operating condition; from human experts, such as engineers with deep and relevant industry domain knowledge; from other similar machines; from other similar fleets of machines; and from the larger systems and environment in which it may be a part of. A digital twin also integrates historical data…

October 29, 2018

A Better Building IoT

October 29, 2018

Technology Powered Services

Control Optimization

AI and data science decode insights in energy data for fault detection and operational benefits

Visualizing energy data to understand usage across portfolios

Buildings can be intelligently and continually controlled with cloud based solutions

Accurate prediction and optimization with automatic actions to achieve maximum energy efficiency

AnalyticsVisualization

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Creating the Digital Twin

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Thermal Model

Historical Power

Historical Weather

Historical BMS

Historical OccupancyBMS Actuals

IoT Actuals Digital Twin

Digital Twin Block Architecture

Tariff

Weather

Control/BMS(data)

CPM.l

CPM.l

CPM.l

…

Comfort/App(data)

CM.l

CM.l

CM.l

…

Assets(data)

FW

FW

FW

…

Meter

Pulse

CT

BMS

…

Smart CityMeta Data

Power Source

IoT Sensors(data)

CM.l

CM.l

CM.l

…

Non-IntegratedPartners/Subs

IntegratedPartner Data)

1

2

3

…

API |

Fee

d

Control Models

Comfort Model

Thermal Model Optimizer

CorrelationEngine

Normalization Engine

Lightweight Appliance

API

Services

Asset Registry

Customer Master

MeterRepositoryTrending

Comms Engine

Analytics/Diagnosis

Power Source

Invtr

Invtr

Invtr

…

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Model

Optimize

Control

Learn

Digital TwinLearning Cycle

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What the Hell is that!?

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©Google DeepMind

Digital TwinsFrom Rules to AI

Artificial Intelligence

Machine Learning

Reinforced LearningTransfer Learning

Extreme Learning Machines

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Leveraging the Digital Twin

• Baseline & Prediction

• Fault Diagnosis

• Commissioning

• Retro-Commissioning

• Demand Response

• Closed-loop Optimization• (with Control)

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Outcome-based Fault Detection

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FDD Can Create Analysis Paralysis

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Outcome-based Fault Detection ServiceChanging the FDD Game

• Closed-loop fault detection, diagnosis, prioritization, and validation service that maximizes the productivity of building facility teams and their contractors

• OFD uses a layered approach of human expertise, rules-based discovery and artificial intelligence to transform the noise of hundreds of detected daily faults into a set of prioritized, and justified, actions

• Builds the deep knowledge base about the building to protect you

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OFD Discovery & Diagnosis HierarchyFrom One Building to Entire Portfolios

MachineLearning

Rules-based FDD

Limited Data/Initial Evaluation

Com

plex

ity o

f det

ectio

n

e.g., Leaking valves, narrow deadbands and simultaneous heating and cooling

e.g., Identifies points and systems with unusual influence over the total building power or other systems. Also finds deviations in

from historical operation.

e.g., Oscillations, flat lines, sensor issues

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Confirming Root Cause Translating analytics to actionable insights

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Oscillation in Chiller1 Differential Pressure

• Simple alarm• Pattern

recognition/change• Manual sweep

…But why is it oscillating?

Step 1 - Correlations MappingOscillation between paired points

This is what

happens when you look at all the data!

Here’s the oscillating

point

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Step 2 - Refine Correlation ValueNode interactions wherein the maximum oscillation is > 0.5

Mid-strength correlations

yield too many possible

dependencies

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Step 3 - Refine Correlation ValueNode interactions wherein the maximum oscillation is > 0.8

High correlation values set priority and

recommended actions

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Step 4 - Diagnosis/Root CauseThree data points impacting anomalous oscillations

Data driven INSIGHT

instead of an overload of information

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Predictive Energy OptimizationFeaturing Demand Response

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Driven by Machine Learning

• Unique thermal model for every building• Machine learning: an ever-evolving model• Model evolves with the building through seasons and

various modes of operation• Capitalizes on every building’s natural thermal

properties

• Incorporates multiple data streams including energy, weather, utility pricing, and BMS points

• Automatic discovery and learning produces truly customized models regardless of equipment types and operations

October 29, 2018

Closed-Loop Control

• Optimized for Cost and Comfort• Micro-adjustments made throughout the day to zone

temperatures and pressures• Shifts HVAC load to less expensive hours• Ensures comfort with individual temperature requirements

for each zone

• Secure and local• Dynamic setpoints delivered via on-site appliance

• Measured for Efficacy• Energy, comfort and operations impact measured• Performance continuously verified via AM&V • Incorporates real-time occupant feedback (future feature)

Model

Optimize

Control

Learn

October 29, 2018

BuildingIQ Control Options

Zone Level• Write Points

• Direct Zone Temperature Setpoint

• Read Points (as available)• Zone Temperature + Setpoint• VAV Points• AHU SAT, SAP, Return/Mixed Air• HVAC Fans Speed & Power• Chilled/Hot Water Valves• Compressor Stage• Damper Positions• Zone Enable + Setpoint• Relative Humidity• Boiler• Chiller

• Required Weather Data• Outdoor Air Temperature

AHU Level• Write Points

• AHU SAT, SAP

• Read Points (as available)• Zone Temperature + Setpoint• VAV Points• AHU SAT, SAP, Return/Mixed Air• HVAC Fans Speed & Power• Chilled/Hot Water Valves• Compressor Stage• Damper Positions• Zone Enable + Setpoint• Relative Humidity • Boiler• Chiller

• Required Weather Data• Outdoor Air Temperature

Model

Optimize

Control

Learn

October 29, 2018

Questions

October 29, 2018

Thank You

October 29, 2018