Building Intelligence for Facility Portfolio Optimization

transcript

Building Intelligence: It’s redefining work

Guy Holden Vice President & General Manager

Global WorkPlace Solutions

Johnson Controls

Johnson Controls, Inc. Proprietary and Confidential Information. Any unauthorized use, copying or distribution is strictly prohibited. 2

90% of all data in the world…

Source: IBM, Getting Ready for Watson

…is less than two years old

This global data

explosion will affect the

way we manage real

estate and facilities

Data from facilities have

many sources

Real Estate profile

Lease Administration

Asset condition

Transactions

Real Estate Data

Budgets / CapEx / OpEx

Spend / CapEx / OpEx

Cost / occupant

Cost / sq. ft.

Costs by building function (e.g. Data Center)

Cost / production unit

Cost allocation / billing

Financial Data

Maintenance Data

Energy Data

Supplier Data

Work order metrics

Asset management

Service delivery performance

Procurement

Demand response contracts

Energy Conservation Projects

Contracts Performance / SLAs

The speed of facility

data is increasing . . .

creating dynamic

data sources

Security Security alarms / events

Policy compliance alerts

Life-safety – fire/medical

Occupancy

Critical Environments

Real time monitoring / trend of building occupancy

Demand-based lighting and HVAC services

Data center performance

Production area performance

24x7 surveillance and remote expert advisor services

Dynamic Facility Data

Dynamic Utility Data

Real time demand

Consumption

Real time pricing

Renewables / performance

Occupant comfort – building environment

HVAC equipment performance

Energy management strategies

Dynamic tuning / optimization

Dynamic set point adjustments

Fail-over backup Operating schedules

Dynamic HVAC Data

Let’s have a look at

some real data . . .

Utilities

Faults Security

Reports

Orders

Financials

Carbon Space

Overview

Enterprise Dashboard

The Dashboard is a high level overview

of key real estate and facility data

Real Estate Leased Facilities

See the real estate portfolio

from multiple views. Search for

properties by

name or address

Or navigate to

properties via the

Filter relevant information by building,

country, region, and other criteria

Real Estate Leased Facilities

Sequentra

Data-driven Real Estate insight

Identification of co-location

and consolidation

opportunities

Quantifiable savings

calculation

Seamless rent and

expense allocations

Track properties in local

currencies, measures,

and languages

Where…

What…

Who…

When…

From the portfolio to

the occupant

Sequentra

Drill into details . . .

Real Estate Energy Spend

Utility spend view

Utilities Analysis

Dynamic Utilities Data

Utilities

Analysis view

HQ is the

largest energy

consumer

The Data Centre is

the most intensive

energy consumer

Utilities Analysis

Dynamic Utilities Data

Utility Data Captured

every 15 minutes Overlaid with

weather data

Benchmark against

multiple facilities

Utilities Analysis

Dynamic Utilities Trending

Filter by facility

Filter by data type

Baseline energy savings shows

the effect of the installation of a

heat exchanger

What happened

Utilities Analysis

Dynamic Utilities Heat Map

Each row is 24

hours (1 day)

All the days

during April

Energy intensity like a

radar map of storm

intensity Why so much

energy being used

overnight?

See the

weekends?

Workplace Motion & All in a Day’s Work

Dynamic Occupancy Data

But data is not enough . . .

Static and dynamic data

sources,

in combination with powerful

software analytics,

can create amazing insights

Data Analytics

Fault Detection and Analysis

Monetization of

persistent fault

conditions

Custom rules configured to

monitor areas of interest

Prioritization of faults

based on severity

Trend fault

occurrences

in time

A simple illustration of

analytics in action . . .

Utility Data

Weather

Meters

Facility

Portfolio Analytics Reporting

£ Above

Baseline

2011 £55,792 +10%

2012 (thru June)

£25,307 +9%

Building Intelligence

Benchmarking and Prediction

Using static data to

create performance

models

Utility Data

Weather

Data Building

Mgmt Data

Meters HVAC Lighting Fire/Security

Business

Systems

Validation &

Payment

Facility

Portfolio Analytics Reporting

Driving Automated Optimization

Blue arrows show

adjustments

automatically created

from the analytics – no

human in the loop

Utility Data

Weather

Building

Mgmt Data

Central

Repository

Analytics Work

Orders

On Site

Work Order Generation Analytics can, in

some cases,

generate

appropriate work

orders based on

analysis of

dynamic data

Utility Data

Weather

Building

Mgmt Data

Central

Repository

Analytics Work

Orders

On Site

Adding Remote Services Remote Services

More complex

analytical results

may require

expert human

interpretation

Utility Data

Weather

Building

Mgmt Data

Central

Repository

Analytics Work

Orders

On Site

Adding Local Services

Remote Services

Local Services

Some real world

examples of the

benefits of static and

dynamic data analytics

Global Data Centre Hosting

Analytics detected occasional

overcooling of racks – wasting energy

and overall cooling capacity

Fuel Stations

Analytics predicted future service

needs of pumps and dispensers

By modeling historical service data it

was shown that service costs could be

reduced with significant increases in

equipment uptime/availability

Convenience Store Coolers

Analytics measure defrost cycles and

temperature trends to optimize

efficiency and predict pending failures

that may result in spoilage

Asset Management

Analytics combine multiple data

sources to model large chillers to

estimate useful remaining life with, and

without, continued maintenance.

Result is a data-driven economic case

for when to repair, and when to

replace.

Service Prioritization

Analytics monitor hundreds of VAV

boxes and identify the poorest

performing 20% along with guidance

on cause.

Building Efficiency

Analytics use utility bill data across an

entire real estate portfolio to model

and identify the least efficient facilities,

probable causes, and estimates of

potential improvement based on

identified remedies.

Service Automation

Analytics review faults and events and

filter to root cause – reducing the

number of work orders generated and

providing more accurate instructions

for issued work orders.

Looking to the future of

Meeting Sensor Detects number of

people at table

Report meeting

attendees vs. capacity

People Counter Detects number of people in

open plan workspaces

Ability to understand peaks and

troughs space usage

Desk Sensor Sensor mounted under each desk

Real time understanding of utilisation

Workplace Motion

How it Works

Potential reduction/spare

capacity in workstations

Workplace Motion

See the Scale of the Opportunity

As it’s not just a snapshot of time ambiguity is eliminated

What you do with the data is key

Real-time dashboard customised to customer needs:

Global/ Regional/ Country/ Building

Trough

Workplace Motion

See Occupancy Trends

Workplace Motion

In-Depth Reporting

Occupied/Vacant % per

building. Ranked by

performance.

Occupied/Vacant % per

organization. Ranked

by performance.

Occupied utilisation

by space types

Standard daily and

weekly utilisation

profile

Workplace Motion

In-Depth Reporting

1 2 3 4 5 5 7 8 9 10 11 12 13 14 15 17 20 21 22 23 25 26 27 30 31 32 33 34 36 37 38 39 40 42 44 45 46 47 48

Occupied Micro absence (15 mins or less) Medium absence (15 mins to 2 hrs) Macro absence (greater than 2 hrs) Unoccupied

What does this mean?

The table above provides additional analysis on the ‘appears occupied’ data, profiling length of absence. The data

suggests absences tend to be either micro (15 minutes or less, shown in purple) or medium (15 minutes to 2 hours, shown

in green) with very small occurrences of macro absences (greater than 2 hours, shown in dark blue).

Workplace Motion

In-Depth Reporting

Workplace Motion

In-Depth Reporting

Building Intelligence is

still in infancy

Extraordinary insights

for facility portfolio

optimization will come

from the correlation of

multiple static and

dynamic data streams

It will redefine the way

we work.

Building Intelligence: It’s redefining work

Learn more

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Building Intelligence for Facility Portfolio Optimization

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