chris irwin - business development director, tridium

The Role of Analytics to Improve Operational Efficiency in Buildings

Chris Irwin October 2016

Business in 2016

• Most processes computerised – ERPs, CRMs etc.

• Massive amounts of data stored

• Business analytics used to provide KPIs

Enterprise Resource Management

Customer Relationship Management

Facilities in 2016

• Some processes computerised – CAFM/CMMS, MWFM etc.

• Massive amounts of data archived

• Little or no analytics applied

Computer Aided Facilities Management

Computerised Maintenance Management Software

Mobile Workforce Management

The “Internet of Things” – term first used by Kevin Ashton in 1999

“is the network of physical objects—devices, vehicles, buildings and other items—embedded with electronics,

software, sensors, and network connectivity that enables these objects to collect and exchange data”

- Wikipedia

New Trends 2016

• Integration

• Convergence

• IoT

• Analytics

Integrated systems

Converged applications

Building Systems

IoT applications

BusinessSystems

PRE-EVENT

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Integration and Convergence

Systems integration in buildings

• HVAC• Lighting• Blinds and shading• Irrigation• Power distribution• Metering• Alarms• Security/Access• CCTV • Fire detection• Smoke dampers• Water leak detection

Facilities Applications

• Standby generators• UPS• Refrigeration• Lifts/escalators• Digital signage• Car parking• Renewable sources

Systems integration in buildings

• Meeting room booking• Hot desk management• Visitor management• Space utilisation planning• Asset management• IT infrastructure• Document management

Business Applications

• CAFM/CMMS• Energy management• Sustainability reporting• Wayfinding• Catering management• ERP• Etc…

Why Integrate?

• Monitor equipment and occupancy in real-time by making use of your existing BMS infrastructure

• Link asset use to control system for improved efficiency

• Follow manufacturer recommendations more closely by generating PM work orders based on run-time

• Depreciate assets based on actual usage

• Avoid manual data transfers from one application to another

• Create potential for more powerful analytics

Example integration with Maximo

• Harnesses the power of IoT and provides an easy to use interface to rapidly discover, connect and monitor Maximo managed Assets, Locations and Meters

• Supports industry standard protocols like BACnet, Modbus, LON, Niagara, oBIX, OPC

• Scalable solution that can handle millions of devices and data points

• Communicates with Maximo via the Maximo Integration Framework (MIF)

Example integration with Maximo

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Real-time Analytics

Connecting “things” results in LOTS of data

Finding what you need can be difficult …

… so we need Analytics – 24/7

Analytics can help find the needle

• Works 24/7

• Identifies patterns, trends and exceptions

• Overcomes the skilled resource issue

• Provides actionable insights

Analytics Capabilities MapDifferent types of analytics

Data

DescriptiveWhat

happened ?

Diagnostic AnalyticsWhy did it happen ?

Predictive AnalyticsWhat will happen ? Decision Action

Analytics Human Input

PrescriptiveWhat should I do ?

Decision Support

Decision Automation

IoT Solutions Business and other analytics solutions generally work at Enterprise and Cloud levels

IoT Solutions Niagara Analytics works at Enterprise and Cloud levels, but can also be deployed on premises at network level running on JACEs)

IoT Solutions In future Niagara Analytics will also work at the ‘edge’ using Niagara Edge technology (due for launch in 2017)

Benefits of Real-time Analytics with a historical perspective

• Live events combined with a history of prior occurrences (e.g., frequency, duration, cost)

• Deeper insight into root causes and proper remediation techniques

• Automatically triggers actions based on a library of formulas unique to your business

Ease of use

• Advanced analytics that do not require specialised programming skills

• Open API supports third-party visualisation and other complementary apps

• Comprehensive business intelligence reporting can be applied to all your operations

How Does Analytics Work?Analytics Overview Data Tagging

is essential

• Collect & arrange data in an organised manner

• Review and assess the data• Come to a result or decision

Actionable results based on real-time data

• trends & forecasting

• “smart alarms”

Analytics vs Alarms

Alarms

• Have to set-up thresholds and alarm definitions in advance

• No intelligence; operator has to interpret

Analytics

• Enables you to find patterns and exceptions

• Can configure rules to make “smart alarms”

• Provides results that show how building is REALLY operating

Analytics vs Alarms - example

Analytics can tell you:

• how many hours in the last 6 months the electrical demand target was exceeded

• how long each of those periods were when they occurred

• what items of equipment were operating when the demand went above the limit

• how those events were related to the weather or building usage patterns

An alarm evaluates a single item against a limit at a single point in time. e.g. an alarm can tell you if energy use is above a specific KW limit right now

PRE-EVENTUse of real-time analytics for fault diagnostics

Analytics for FM – fault diagnosticsTraditional process


BMS generates one or more alarms



Maintenance Manager decides what to do




Works Order raised




Works Order raised

Site visit to assess




Works Order raised


Request for replacement




Works Order raised


Request for replacement

2nd site visit to install replacement

with Analytics process


Analytics diagnoses fault

Works Order raised automatically

Replacement item ordered automatically

Site visit to repair

Analytics Application Example:Air Handler: HWS / CHWS Diagnostic

Air Handler indicates it is operating according to desired set-point. CHW

valve is working and supply temperature is within tolerance of the set point.

Analytics Application Example:Air Handler Performance appears to be normal

Set-pointSupply Temp

0:00

2:00

4:00

6:00

8:00

10:00

12:00

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16:00

18:00

20:00

22:00

0

10

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Fan Status

Analytics Application Example:Air Handler: HWS / CHWS Diagnostic

Air temperature is getting warmer after the Heating Coil with the Hot Water Valve fully closed indicating a mal-functioning

Hot Water Valve

Analytics Application Example:Air Handler Performance with diagnostics

Set-pointSupply Temp

Fan StatusMixed Air TempHeat Coil Air Temp

0:00

2:00

4:00

6:00

8:00

10:00

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0102030405060708090

Hot Water Valve

Value PropositionAlerts on trends and exceptions

• Know what you need to know and when you need to know

• Let the system take corrective action when possible

• Report results and “smart alarms”

How to implement analytics for fault diagnostics

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• Find an expert to identify opportunities for improvement, who knows how to identify faults

• Create algorithms using analytics tool to implement the judgement/decision process in software

• Run the algorithms on the real-time data • Send analytics results to FM works order

management application• Automatically generate works orders with

replacement parts required already identified

Select an Algorithm

Choose required

algorithm from library, or

create new one

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Sample AlgorithmExcerpted from Niagara Analytics Framework 2.0 Reference

Low chilled water temperature algorithm

Selected Algorithm - created in Niagara Wire sheet

Pick Buildings - configure an alert

Visualise and Analyse - alerts at a building level

Visualise and Analyse

raw AHU data before Analytics

AHU data after Analytics algorithm has run

Act - Intelligent Alerts Message View

Leakage of Hot Water in Hot Water Valve Detected. May cause a rise in Energy Consumption for this zone

PRE-EVENTUse of real-time analytics in space utilisation

Example – hot desk managementProblem definition:• Don’t know how many desks are being used each day

or for how long• Could have IT application for recording log-in times

per IP address – not adequate • Useful to know patterns and movement correlations

across multiple users

Example – hot desk management

REST based APIIoT App

BMS

Wireless mesh networked sensors

under desks



BMS

3G/4G comms or IP to link to Cloud



BMS

Integration with BMS for equipment status and control



BMS

Cloud management of

hot desking



BMS

Data sharing with other apps

Advantages• Real-time data on building utilisation• Can combine with “logged on” status• Can link to BMS to adjust environmental conditions• Enables space optimisation by department/floor etc.• Analytics can process all the data to identify issues

and flag exceptions• Multi-sensors can provide data for other applications• Integration of all data sets is vital to inform decision-

making

Summary• Cost of sensors and communications technology is

falling while sophistication of s/w is increasing• Previously siloed applications are converging and can

be integrated, so real-time data is shared• Analytics algorithms can automate fault diagnostics

and energy optimisation• Energy and maintenance costs can be reduced and

assets managed more cost-effectively• Space utilisation can be monitored in real-time and

optimised using analytics

PRE-EVENTQuestions?