Chris Irwin - Business Development Director, Tridium
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The Role of Analytics to Improve Operational Efficiency in Buildings
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Chris Irwin - Business Development Director, Tridium
- 1. The Role of Analytics to Improve Operational Efficiency in Buildings Chris Irwin October 2016
- 2. 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
- 3. 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
- 4. 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
- 5. New Trends 2016 • Integration • Convergence • IoT • Analytics Integrated systems Converged applications Building Systems IoT applications Business Systems
- 7. 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
- 8. 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…
- 9. 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
- 10. 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)
- 11. Example integration with Maximo
- 13. Connecting “things” results in LOTS of data
- 14. Finding what you need can be difficult … … so we need Analytics – 24/7
- 15. Analytics can help find the needle • Works 24/7 • Identifies patterns, trends and exceptions • Overcomes the skilled resource issue • Provides actionable insights
- 16. Analytics Capabilities Map Different types of analytics Data Descriptive What happened ? Diagnostic Analytics Why did it happen ? Predictive Analytics What will happen ? Decision Action Analytics Human Input Prescriptive What should I do ? Decision Support Decision Automation
- 17. IoT Solutions Business and other analytics solutions generally work at Enterprise and Cloud levels
- 18. IoT Solutions Niagara Analytics works at Enterprise and Cloud levels, but can also be deployed on premises at network level running on JACEs)
- 19. IoT Solutions In future Niagara Analytics will also work at the ‘edge’ using Niagara Edge technology (due for launch in 2017)
- 20. 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
- 21. 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
- 22. 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”
- 23. 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
- 24. 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
- 25. PRE-EVENTUse of real-time analytics for fault diagnostics
- 26. Analytics for FM – fault diagnostics Traditional process
- 27. Analytics for FM – fault diagnostics Traditional process BMS generates one or more alarms
- 28. Analytics for FM – fault diagnostics Traditional process BMS generates one or more alarms Maintenance Manager decides what to do
- 29. Analytics for FM – fault diagnostics Traditional process BMS generates one or more alarms Maintenance Manager decides what to do Works Order raised
- 30. Analytics for FM – fault diagnostics Traditional process BMS generates one or more alarms Maintenance Manager decides what to do Works Order raised Site visit to assess
- 31. Analytics for FM – fault diagnostics Traditional process BMS generates one or more alarms Maintenance Manager decides what to do Works Order raised Site visit to assess Request for replacement
- 32. Analytics for FM – fault diagnostics Traditional 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
- 33. Analytics for FM – fault diagnostics Traditional 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
- 34. 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.
- 35. Analytics Application Example: Air Handler Performance appears to be normal Set-point Supply Temp 0 10 20 30 40 50 60 70 80 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Fan Status
- 36. 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
- 37. Analytics Application Example: Air Handler Performance with diagnostics Set-point Supply Temp Fan Status Mixed Air Temp Heat Coil Air Temp 0 10 20 30 40 50 60 70 80 90 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Hot Water Valve
- 38. Value Proposition Alerts 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”
- 39. How to implement analytics for fault diagnostics 39 • 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
- 40. Select an Algorithm Choose required algorithm from library, or create new one
- 41. 41 Sample Algorithm Excerpted from Niagara Analytics Framework 2.0 Reference Low chilled water temperature algorithm
- 42. Selected Algorithm - created in Niagara Wire sheet
- 43. Pick Buildings - configure an alert
- 44. Visualise and Analyse - alerts at a building level
- 45. Visualise and Analyse raw AHU data before Analytics AHU data after Analytics algorithm has run
- 46. Act - Intelligent Alerts Message View Leakage of Hot Water in Hot Water Valve Detected. May cause a rise in Energy Consumption for this zone
- 47. PRE-EVENTUse of real-time analytics in space utilisation
- 48. Example – hot desk management Problem 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
- 49. Example – hot desk management REST based API IoT App BMS Wireless mesh networked sensors under desks
- 50. Example – hot desk management REST based API IoT App BMS 3G/4G comms or IP to link to Cloud
- 51. Example – hot desk management REST based API IoT App BMS Integration with BMS for equipment status and control
- 52. Example – hot desk management REST based API IoT App BMS Cloud management of hot desking
- 53. Example – hot desk management REST based API IoT App BMS Data sharing with other apps
- 54. 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
- 55. 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
Notas do Editor
- This is a representation of a typical IoT solution with the many layers require to get data from many desperate devices to either a Desktop UI or Mobile device. Traditional analytics are located at the Enterprise and Cloud layers. These products collect historical data and do analysis across a broad range of devices collecting data. Tridium’s NA can also exist at this layer providing the same solution as other products in the market However, with NA, the designer can move the analytics functions down to the local premises controller (Jace) taking advantage of running analytics closer to the edge. By doing so, unlike traditional analytics products, NA has access to real-time data. This allows the designer to create complex algorithms based on real-time data which can be used to improve operational efficiencies. As an example, the NA application could look across all space conditioning system (VAV’s) from a high level, and make real time set-point adjustments to operate heating and cooling equipment (AHU, Rooftop Unit) at peak energy saving based on real-time characteristics. Tridium is also working on a new generation of edge device solutions which will ultimately allow the designer to move the NA application further down the IoT layer stack.
- This is a representation of a typical IoT solution with the many layers require to get data from many desperate devices to either a Desktop UI or Mobile device. Traditional analytics are located at the Enterprise and Cloud layers. These products collect historical data and do analysis across a broad range of devices collecting data. Tridium’s NA can also exist at this layer providing the same solution as other products in the market However, with NA, the designer can move the analytics functions down to the local premises controller (Jace) taking advantage of running analytics closer to the edge. By doing so, unlike traditional analytics products, NA has access to real-time data. This allows the designer to create complex algorithms based on real-time data which can be used to improve operational efficiencies. As an example, the NA application could look across all space conditioning system (VAV’s) from a high level, and make real time set-point adjustments to operate heating and cooling equipment (AHU, Rooftop Unit) at peak energy saving based on real-time characteristics. Tridium is also working on a new generation of edge device solutions which will ultimately allow the designer to move the NA application further down the IoT layer stack.
- This is a representation of a typical IoT solution with the many layers require to get data from many desperate devices to either a Desktop UI or Mobile device. Traditional analytics are located at the Enterprise and Cloud layers. These products collect historical data and do analysis across a broad range of devices collecting data. Tridium’s NA can also exist at this layer providing the same solution as other products in the market However, with NA, the designer can move the analytics functions down to the local premises controller (Jace) taking advantage of running analytics closer to the edge. By doing so, unlike traditional analytics products, NA has access to real-time data. This allows the designer to create complex algorithms based on real-time data which can be used to improve operational efficiencies. As an example, the NA application could look across all space conditioning system (VAV’s) from a high level, and make real time set-point adjustments to operate heating and cooling equipment (AHU, Rooftop Unit) at peak energy saving based on real-time characteristics. Tridium is also working on a new generation of edge device solutions which will ultimately allow the designer to move the NA application further down the IoT layer stack.
- Alarming is the way building management systems have traditionally been managed, with the facilities manager or team responding to alarms by investigating the causes. The usual problem is that the alarm threshholds have to be set-up at commissioning and there are frequently too many alarms, so they get switched off. Alarms are pretty dumb – the human operator has to interpret the meaning. In contrast with analytics, one can configure formulae to analyse the alarms and deduce what the problem is. One can intelligently process multiple alarms to reduce the “clutter” that is the usual problem experienced by those trying to mange a BMS. Analytics makes alarming “smart”.
- Contrast a simple KW threshold alarm and a an analytics smart alarm
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- I expect most of you are already familiar with dashboards and graphs that let you visualise the data from your building, but the limitation of a visual display is that someone with a high skill level needs to look at the graphs and interpret what is going on. As humans we get tired, and we cannot be reviewing the data all the time. In contrast an analytics program works 24/7, and applies the same analysis consistently.
- Looking at the Air Handler, it appears to be functioning in a cooling mode as expected at first glance. Supply temperature is within an acceptable tolerance with respect to the Set-point. Chilled Water Valve is open and Hot Water Valve is closed.
- Fan is on and the current supply temperature is within an acceptable range of the set-point.
- The hot water valve must be defective and leaking when it is assumed it is closed. This is very inefficient, and causes additional cooling requirements to overcome the leaking hot water valve. Over time, the wasted energy to overcome the leaking valve can be costly. NA can constantly monitor these temperatures and notify the facility manager of these types of situations using fault diagnostics.
- However, taking a deeper look, the temperature rises after moving through the heating coil. Currently, the hot water valve is fully closed. What could be causing this?
- Niagara Analytics comes with a list of pre-canned Automated Fault Detection and Energy Savings Algorithms for a user to select from These algorithms help you to easily apply them to the devices & equipment that you want to monitor for the specified condition and ensure you have a constant pulse on your system You also have flexibility to create your own personal algorithm library based on your specific system requirements and needs Here I am selecting an Algorithm that detects a leaking valve in an AHU
- Just like the Leaking Valve algorithm selected above by me, Algorithm and Algorithm blocks allows a user to create powerful statistical algorithms to suit their domain needs, be it HVAC or Security As you see from here, these algorithms are created using the native Niagara wiresheet programming logic This is a drag-and-drop interface with no need to learn any new complex programming language or code Plus this methodology allows you to leverage the power of the entire Niagara community
- Here I am selecting the Buildings for which I want Niagara Analytics Engine to check for a leaking valve in an AHU. I have opted to run this algorithm on all the Tridium Buildings across US and UK (say 7 buildings in each country). I can exclude certain zones or equipments if required. I am putting a cost of 2 to each occurrence of Leaking valve in my historical data. This cost can, at a later stage, help me in prioritizing the alarms I can also set the amount of data to be analyzed and frequency of analysis Alert configuration provides you with the flexibility to select and customize the equipment & nodes you want monitored, where in the hierarchy you want it monitored, assign cost based on importance of the equipment in your system, and how often you want it monitored (based on # of occurrences or based on # of hours the alert is true)
- In this view, I am using N4 Hierarchy to see alerts at Country, State, Equipment or even at sensor level. I started with Tridium Buildings and see all the things I am monitoring on this hierarchy as well as which of these are in alert mode Here I see the alert for a Leaky valve (based on the algorithm that I picked in the earlier step) with a cost of $240. Clicking on the US level I got to know that a situation of a Leaking Valve exists with its HVAC system.
- Once I click on the Leaking Valve alert, I can see the data related to the alert such as Mixed Air Temperature, Hot water Deck and Heating Valve Position However if I am monitoring data over a longer period of time, it becomes really hard to visually analyze the information and determine the correlation and differences In this example, mixed air and hot water deck temp look like they are in sync with each other and heating valve position is closed
- If I go back to the Alarm Console, I also got an Alarm which has a list of steps to rectify the issue This alarm is raised by Alert Engine which intelligently sums up the abnormal occurrences for the user and raises an alarm once it crosses a given threshold. So a user can sit back and relax and let the Niagara Analytics monitor and analyze your devices for you.