IBM Intelligent Building Management 2012

1 © 2010 TRIRIGA INC. | TRIRIGA PROPRIETARY AND CONFIDENTIAL © 2012 IBM Corporation

IBM Smarter Buildings February 2012


The need for progress is clear

2025By 2025, buildings worldwide will become the top energy consumers.

1Buildings are the number 1 contributor to global CO2 emissions.

2Real estate is the second largest expense on the income statement for most companies.

In most organizations the real estate portfolio is on the balance sheet as the third most valuable or expensive single asset.

3

42 percentWorldwide, buildings consume 42% of all electricity – up to 50% of which is wasted.

30 percentFacilities investments and operating costs can be more than 30% of corporate annual spending.


The Environment for Property Management

• How do I achieve a better size/quality portfolio at lower cost?

• How do I maximise space utilisation?

• Does my portfolio meet future business requirements?

• Should I buy/lease this new space or not?

• Are operations costs minimised without compromising my business?

• Are my staff and assets safe and secure?

• How do I meet legal/corporate energy, waste and environmental goals?

• How do I facilitate efficient moves and changes?

• Are we getting good reliable, consistent facilities service?

• Is there data to keep control and competitively re-tender facilities services?

Businesses are under increasing pressure to reduce cost, improve service and demonstrate environmentally responsible practices.


What is a Smarter Building?

Smarter Buildings…

Are more cost effective by reducing energy and operating costs.

Use active and designed-in techniques to achieve reliability, efficiency and environmental responsibility.

Provide Visibility, Control and Automation to building systems.

Maintain a safer and more secure workplace.

Communicate in real-time to supporting infrastructure (i.e. smart grid, broadband, etc.).

Smarter Buildings are well managed, integrated physical and digital infrastructures that provide optimal occupancy services in a reliable, cost effective, and sustainable manner.


Exte

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IBM Intelligent Buildings solutions – the smarter building solution

WaterSmart Meters,

Use / Flow Sensing

EnergySmart Meter,

Demand Response ElevatorsMaintenance,Performance

HVACFans, Variable Air

Volume, Air Quality

LightingOccupancy

Sensing

Access/SecurityBadge In,

Cameras, IntegrationPerimeter, Doors,Floors, Occupancy

Space UtilizationOccupancy/

Management

24/7 MonitoringCondition Monitoring,Parking Lot Utilization

WeatherCurrent

Predictions

Emergency

ServicesAlerts, Actions

UtilitiesDemand Mgmt,

Cost ControlCommunit

yServices

Transportation,

Traffic, EventsCommerci

alPotential

Advertisement

Voice/Video/Data

Integrated Building & Communications Services

PortfolioRE Mgmt

Asset MgmtLifecycle

Energy UsePassive/Active

OccupancySpace Mgmt

ComplianceReal Estate Mgmt

Building Services

Maintenance/Projects

Tenant ServicesHelp Desk

Waste MgmtTrash/Water/Recycle

Industry Specific

Hospital, Hotel, Etc.

Integrated Building & Communications Services

Fire FunctionalityChecks,

Detector Service


IBM Intelligent Building Management

Visualization

Intelligent

Interconnected

Instrumented

Physical

Roles-based dashboards

Real-time analytics

Comparison of historical data with newly collected data

Energy and Operations optimization. Alerts & Work order Automation

Real-time monitoringData aggregation from multiplebuildings and systems

Connectivity to BMSs & Meters

Vendor Building Management Systems

Data aggregation – single building

Sensors and SystemsField level devices


Case Studies: IIBM’s value proposition

IBM Rochester, 3.3M sq ft multi-building mixed use light industrial campus. Facilities date to the 1950s. Consistently achieved year on year energy reductions of 5% to 7% over the last 10 years.

• Energy cost reduction on equipment monitored of between 10-15%

• Reactive maintenance decreased by 16%• Hours per work order reduced by 34%• Total number of work order hours decreased by 49%

Tulane University

Rochester, MN

In the wake of Hurricane Katrina’s destruction in 2005, Tulane University saw an opportunity and a responsibility to advance campus-wide sustainability and energy

efficiency. • Over 50% reduction in energy cost since the system was

installed

• Tulane can collect, manage and analyze data from buildings to gain intelligence and insight to energy and facilities management.

Client A

A global life sciences company recently completed a PoC, in their tightest controlled building environment. The findings were therefore based on the best case scenario within Client A’s buildings.

• Energy savings benefits from PoC are estimated to be c. €57,000 pa.

• Sufficient business case justification provided to move to the next stage and a new building.


Analytics are a key component for managing buildings

4. Improvement

3. Analytics

2. Data Store

Enterprise Reporting

Feed

Statistics/Models for Improvement

Work Orders, Excel, Browser, Planning

IdentifiedImprovement

actions/projects

1. Sensing-Monitoring

Targeted data acquisition

Meta-data model• Contextualization• Interrelationships

5. Sustainability & Reporting

Agnostic software solutionCan push data from any BMS

No requirement for a hardware bridge

Rule 2a 2b 3a 3b 4c 4d 4e 6a 7a 7b 8a 8b 8c 8d 10a 18 19 21e 21f Time Period

Severity (5=Highest) 5 5 3 3 3 3 5 5 5 5 3 4 3 4 3 3 3 5 5 Rolling 7 days

Savings Value (H-igh,M-edium) H H H H H H H M M M M M From

Savings Factor (months) 6 6 6 6 6 6 6 12 12 12 12 12 12 12 12 8/24

Start Day 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 To

Start Month 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8/30End Day 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30

10 to 25 End Month 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 25 to 50

25 to 50 50 to 100

>50 >100

Color Coding: Alerts by

Rule Asset

2a 2b 3a 3b 4c 4d 4e 6a 7a 7b 8a 8b 8c 8d 10a 18 19 21e 21f

Color Coding: Alerts by Asset

RCHB0201527 0 0 0 11 0 0 0 0 10 30 15 28 0 53 0 0 6 0 3 113

RCHB0201529 0 0 0 46 0 0 0 91 50 41 41 60 0 105 0 0 0 0 7 340

RCHB0303572A 0 3 2 7 0 0 0 0 0 2 16 19 0 0 0 4 0 0 46 64

RCHB0303574A 0 0 0 12 0 0 0 0 5 11 ##### ##### 0 0 0 4 3 0 66 101

RCHB0303575A 0 0 0 12 0 0 0 4 5 15 31 53 0 0 0 0 26 5 2 69

RCHB0401565 0 0 0 41 0 0 0 0 0 0 ##### ##### 0 0 13 0 0 0 8 62

RCHB0402505 0 0 0 38 0 0 0 0 0 0 ##### ##### 0 0 0 0 0 0 8 46

RCHB0403509 0 0 2 38 0 0 0 0 0 0 ##### ##### 0 0 0 0 0 0 8 48

RCHB0403552 0 0 0 0 0 0 0 0 0 0 ##### ##### 0 0 0 0 0 0 8 8

RCHB0503550 0 0 0 15 0 0 0 2 2 0 65 69 0 0 0 20 0 0 27 66

RCHB1011003 0 0 0 25 0 0 0 0 0 0 87 118 0 0 0 0 0 1 4 30

RCHB1011004 0 0 0 22 0 0 0 0 0 0 ##### ##### 0 0 0 0 0 1 4 27

RCHB1011013 0 0 0 10 0 0 0 0 15 5 ##### ##### 0 0 0 0 0 0 86 116

RCHB102MJ49AC2 0 3 13 9 0 0 0 0 0 0 55 113 0 25 0 0 0 0 106 156

RCHB102MK49AC1 0 0 0 25 0 0 0 0 0 0 ##### ##### 0 9 0 0 0 0 123 157

RCHB1032020 0 0 0 0 0 0 0 62 20 32 13 86 0 30 0 24 0 0 63 231

RCHB1032021 0 0 0 12 0 0 0 62 20 32 80 108 0 30 0 0 0 0 63 219

RCHB1051001A 0 0 0 29 0 0 0 0 0 0 ##### ##### 0 0 0 0 0 0 146 175

RCHB105MK70AC10 0 15 14 49 0 0 0 0 0 0 58 116 0 117 0 0 0 0 167 362

RCHB1091007 0 0 0 6 0 0 0 0 0 0 ##### ##### 0 0 0 8 0 0 0 14

RCHB1092042 0 0 0 16 0 0 0 0 0 0 ##### ##### 2 0 0 0 0 0 167 185

RCHB1141003 32 28 14 0 0 0 0 0 0 0 46 49 0 0 0 0 0 0 3 77

RCHB1141005 0 0 0 6 0 0 0 0 0 0 61 64 0 0 12 0 0 0 4 22

RCHB1141021 0 0 0 0 0 0 0 0 0 0 54 64 0 0 0 22 0 0 64 86

RCHB1142052 0 0 0 9 0 0 0 0 0 0 60 62 0 0 0 0 0 0 59 68

RCHB1142053 0 0 0 1 0 0 0 20 7 35 111 114 0 1 0 2 0 0 89 155

RCHB1142054 0 0 0 11 0 0 0 0 0 0 ##### ##### 0 0 0 0 0 0 59 70

RCHB1142055 0 0 0 11 0 0 0 0 0 21 25 36 0 0 0 0 0 0 82 114

RCHB203M010 0 0 0 3 0 0 0 0 0 0 ##### ##### 0 64 0 0 27 0 4 98

RCHB203M014 0 0 0 11 0 0 0 61 45 32 ##### ##### 0 76 0 0 0 0 4 229

RCHB203M015 8 9 3 6 0 0 0 0 2 16 ##### ##### 0 36 0 0 10 0 0 90

RCHB203M018 0 0 0 0 0 0 0 0 0 0 ##### ##### 0 65 0 0 0 0 4 69

RCHB203M038 2 1 0 0 0 0 0 0 20 4 2 2 0 62 0 0 0 0 0 89

54 70 71 785 0 0 0 302 201 278 ##### ##### 22 728 30 175 ##### 7 1611 4513

Daily Usage Summary

Sort Capability

Year To Year Usage

Daily Alert Summary

Alert DetailTrending


Smarter building saving model

Baseline Energy Maint. Activity

New Information

Drives Savings & more Work Orders

Areas of Maintenance Savings• Root Cause Identification• Fewer Total Customer Calls• Reduced PM’s


ALERT ACTION SAVINGS YR-1

Site 1: AHU simultaneous heating and cooling

•Created program to keep AHU’s from heating & cooling at the same time

$3k

Site 2: 1 Variable speed drive running fan at 90% capacity for more than 2 hours

• Replaced damaged duct work• Repositioned sensor

$10k

Site 3: 3 AHUs running outside scheduled hours

• Control panel software failure prevented AHUs from turning off at normally scheduled time

• Performed cold start on panel to clear problem

$18k

Site 4: Electrical current detected when AHU is supposed to be in off mode

• AHU had been manually set to run constantly• Rest to cycle on and off as dictated by the

central control system$19k

Rapid Savings: Small sample of results for the first month for Phase 1

IBM Internal Examples

Expected Savings: 10% - 15% Annual Saving For Equipment Covered By Analytics

Smart Building Results: Energy


-100%

-50%

0%

50%

100%

150%

200%

Prior Yr QtrlyAve

4Q10 1Q11 2Q11 3Q11

# of Work Orders Work Order Hours

- 16%

Pre IIBM Post IIBM

Ave Hr/WO

- 34%

- 49%

Work Order # & Hours Average Hours Per WO

Demonstrated Maintenance efficiency

1) 16% Reduction in total number of reactive work orders generated

2) 34% Reduction in the average time spent per work order

3) 49% Reduction in total work hours

Smart Building Results: Maintenance


Return on Investment – IBM

• Factors that will Influence Site Specific Results:

• Implementation Cost:– Level of Existing Instrumentation– Building Management System Sophistication– Work Order Logistics System– Skilled Staff Availability

• Benefits:– Maintenance Productivity– Equipment Efficiency Levels– Depth of Implementation– Type of Space (Office, Data Center, etc.) INSTRUMENTED

INTERCONNECTED

INTELLIGENT

First Step: - Select a Building - Assess Starting Point

One Year Payback


Makuhari, JPEmb Golf Links, INEmb Manyata, INEhningen, GerMonpellier, FRHursley, UKLennox Wood, UKPortsmouth, UKSouth Bank, UKHortolandia, BRBromont, QUBurlington, VTColumbus, OHGaithersburg, MDMarkham, ONSterling Forest, NYAlmaden, CATucson, AZ

IBM: Phased Implementation Approach

Silicon Valley Lab, CA

Dublin, Ireland

Legacy Mfg Plant

Initial Green Sigma Rules Development

Software Development

Initial Test Location

Rochester, MN

3.2M SF Multi-Use Facility

6th Largest IBM Energy Consumer

Full Functionality Pilot

Global Corp-orate HQ

Smarter Building Showcase

Armonk, NY

Deploy At HighestEnergy Consuming Locations

2009 2010 2011 2012

Austin, TXBoulder, COFishkill, NYPoughkeepsie, NYRaleigh, NCSouthbury, CTYorktown, NY

Deploy at High Energy Consuming Locations & across all geographies


Benefits

The IBM Smarter Building solutions — which combines IBM’s software, analytics

and services expertise together with industry-leading business partners — enable

clients to: Manage energy use, lower base-line costs and decrease emissions by monitoring and analyzing heat, air conditioning and power consumption

Maintain equipment proactively, identifying emerging problems and trends to prevent breakdowns and confirm that critical assets will work as needed

Lower maintenance and building management costs and extend asset life through condition-based maintenance, greater insight into asset conditions and automated notification when assets are performing outside of specifications

Reduce operating costs by managing facilities and infrastructure, covers breadth of O&M, pre-built performance metrics, embedded industry benchmarks, multi-dimensional analysis, single view of information


Questions?

THANK YOU!

IBM Intelligent Building Management 2012

Technology

smart building

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tririga proprietary

smarter buildings

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