the journey - osisoft · defining the journey ... (mixed use class a, research, manufacturing) san...

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Find va lue locked

away in the da ta

T h e J o u r n e y

Scott Smith, Industry Principal Facilities and Data Centers

June 2019

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▪ Defining the Journey

▪ Example Approaches

▪ Eli Lilly (Mixed Use Class A, Research, Manufacturing)

▪ San Leandro Tech Campus (Class A)

▪ UC Davis University (Mixed Use, Thermal Plant, Power, Campus)

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AGENDA

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Defining the Journey

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Today Future

Journey

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Today – Balancing Act

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Comfort/ Condition Requirements

ReliabilityEfficiency

Ongoing Projects

Resources

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Future - Defining

Predictive Analytics

Machine Learning

Self-Service

Integration

NetZero Resources

………….

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Journey – Starts with Data

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80%Facility Managers expect

the demand for data to be greater over the next 5

years*

58%

38%

57%

65%

*2018 Survey of 700 Facility Managers

Analyze performance data either upon failure, never or once a year

Analyze logs of system changes either upon failure, never, once a year or greater

Have not completed a cyber-security assessment

Have no plans for Smart Technologies

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Data Driven – First Steps of the Journey

Fault Detection Diagnostics

Condition Based Maintenance

Energy ManagementCyber-Security

Situational Awareness

CMMS

Cloud

Geo-Spatial

IOTSustainabilityAsset Management

Resilience

Automation

Reliability

Outsourcing

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CHALLENGES

AHEAD

Resources3

Costs2

OutsourcingFear of TechnologyLack of TechnologyResistance to ChangeLack of Awareness/Compelling EventMission Critical ValueShiny Object SyndromePrior FailuresOwner – Tenant Structure

(Cost vs Benefit)

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Today FutureJourney

Think Big – Execute SmallDefine Measurable ObjectivesFollow the Data

Packing for your Journey

Invest in ResourcesPrioritize Stakeholder Support

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Examples

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Business Need

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Monitoring

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Faults

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Results

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Efficiency in design

• Old HQ – 26 kWh Sq/Ft

• SLTC – 8.5 kWh/Sq/Ft

• Dynamic Window Tinting

• Neolith Tiles

• LED Lighting

San Leandro Tech Campus Project:1 M Sq Ft Mixed Use Development

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CHALLENGES SOLUTION BENEFITS

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▪ Design vs. actual

performance

▪ Failed equipment

▪ Inadequate or

missing

specifications

▪ Implement PI for

benchmarking the

HVAC, hot water

and window tinting

system.

▪ BACnet

▪ UFL

▪ PI Vision

▪ Found numerous gaps between the design specification and the actual system requirements.

▪ Failed or poorly installed equipment

▪ Programming errors

▪ Building design issues

Results

Problems with the real world - Issues they didn’t foresee

We allowed user overrides for temperature controls from 68 to 74 degrees

▪ No Global Reset was included in scope to the reset the building to target temperatures each night.

▪ Open floorplan and creating heating and cooling competition between VAV boxes.

▪ Reset to 73 degrees reduced complaints and comfort issues.

Post Commission Analysis

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CHALLENGES SOLUTION BENEFITS

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▪ Design vs. actual

performance

▪ Failed equipment

▪ Inadequate or

missing

specifications

▪ Implement PI for

benchmarking the

HVAC, hot water

and window tinting

system.

▪ BACnet

▪ UFL

▪ PI Vision

▪ Found numerous gaps between the design specification and the actual system requirements.

▪ Failed or poorly installed equipment

▪ Programming errors

▪ Building design issues

Problems with the new world - Issues they didn’t foresee

▪ Initially we allowed user overrides – but

that caused HVAC issues

▪ Individual window issues/override issues

▪ Initial IR sensor had issues with intermittent

cloud cover

How tinting should work

▪ 4 Tint Levels

▪ Follow the Sun

Single Pane of Glass

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CHALLENGES SOLUTION BENEFITS

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▪ Design vs. actual

performance

▪ Failed equipment

▪ Inadequate or

missing

specifications

▪ Implement PI for

benchmarking the

HVAC, hot water

and window tinting

system.

▪ BACnet

▪ UFL

▪ PI Vision

▪ Found numerous gaps between the design specification and the actual system requirements.

▪ Failed or poorly installed equipment

▪ Programming errors

▪ Building design issues

Individual floors stopped responding to the pre-conditioning

The challenge

▪ 28% of the zones never make it to set point.

▪ The analysis highlighted fundamental issues:

▪ The user control option to 68 degrees is 2 degrees cooler than the design of the building allowed.

▪ Building pre-conditioned 1.5 hours prior to occupancy.

▪ Identified asset faults

Performance Monitoring – The Start of Machine Learning

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CHALLENGES SOLUTION BENEFITS

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▪ Design vs. actual

performance

▪ Failed equipment

▪ Inadequate or

missing

specifications

▪ Implement PI for

benchmarking the

HVAC, hot water

and window tinting

system.

▪ BACnet

▪ UFL

▪ PI Vision

▪ Found numerous gaps between the design specification and the actual system requirements.

▪ Failed or poorly installed equipment

▪ Programming errors

▪ Building design issues

Individual floors stopped responding to the pre-conditioning

▪ Measuring Occupancy from a combination of sound and light

▪ Event Notifications when a user leaves a light on after the end of the meeting

▪ Measure utilization of conference room to assign a temperature set point specific to the demand of the room

IoT Sensors - $150 Portable Troubleshooter

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UC Davis: Meter to Meter Quality

• 1 Building is Useful

• Comparing finds patterns

• 2 Buildings Energy Balance

• 1 Building CHW does not follow pattern

• Meter issue

Outside Air Temperature [ºF]

Outside Air Temperature [ºF] Outside Air Temperature [ºF]

Outside Air Temperature [ºF]

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UC Davis: WIFI To Schedule

• Compare WIFI to Schedules

• Identify Schedule Savings Opportunities

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UC Davis: ∆T

• Identify ∆T for each building

• Identify load and flow use

• Identify targets based on value

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Summary

Today FutureJourney

• Only you know your challenges – Solution fits your need /not fitting your facility to their functions

• Small measurable objectives – Finding value• Follow the Data

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Scott Smith

ssmith@osisoft.com

Industry Principal, Facilities and Data Centers

OSIsoft, LLC

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