chris irwin - business development director, tridium
TRANSCRIPT
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
Analytics for FM – fault diagnosticsTraditional process
BMS generates one or more alarms
Analytics for FM – fault diagnosticsTraditional process
BMS generates one or more alarms
Maintenance Manager decides what to do
Analytics for FM – fault diagnosticsTraditional process
BMS generates one or more alarms
Maintenance Manager decides what to do
Works Order raised
Analytics for FM – fault diagnosticsTraditional process
BMS generates one or more alarms
Maintenance Manager decides what to do
Works Order raised
Site visit to assess
Analytics for FM – fault diagnosticsTraditional process
BMS generates one or more alarms
Maintenance Manager decides what to do
Works Order raised
Site visit to assess
Request for replacement
Analytics for FM – fault diagnosticsTraditional process
BMS generates one or more alarms
Maintenance Manager decides what to do
Works Order raised
Site visit to assess
Request for replacement
2nd site visit to install replacement
with Analytics process
BMS generates one or more alarms
Analytics diagnoses fault
Works Order raised automatically
Replacement item ordered automatically
Site visit to repair
Analytics for FM – fault diagnosticsTraditional process
BMS generates one or more alarms
Maintenance Manager decides what to do
Works Order raised
Site visit to assess
Request for replacement
2nd site visit to install replacement
with Analytics process
BMS generates one or more alarms
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
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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
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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
Example – hot desk management
REST based APIIoT App
BMS
3G/4G comms or IP to link to Cloud
Example – hot desk management
REST based APIIoT App
BMS
Integration with BMS for equipment status and control
Example – hot desk management
REST based APIIoT App
BMS
Cloud management of
hot desking
Example – hot desk management
REST based APIIoT App
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?