True Analytics™ - Energy Savings, Comfort, and Operational Efficiency
Pushing the Envelope
Building analytics beyond HVAC
Jim Sinopoli PE, LEED BD+C, RCCD
Smart Buildings LLC
Over the recent past, the best use of an analytic software application for building systems has been fault detection and diagnostics (FDD) specific to HVAC systems. There is research and a number of case studies with verified results showing analytic software reduces energy consumption, improves the efficiency and effectiveness of building operation, and reduces building OPEX. Once used, FDD becomes a core operational tool for many facility managers.
the impressive progress with FDD, the industry is in its infancy of
utilizing data analytic applications in buildings. If analytics for the
HVAC system has provided outstanding outcomes, we need to take that
template to other building systems.
Analytic applications are based on “rules” of how the system should optimally operate, generally obtained from the original design documents and monitoring key data points in near real-time. Essentially you compare the real-time data with the rules and if the data adheres to the rule, the system is fine; if not, the system is not running optimally and has a fault. For those systems that are not process based, applying analytics generally uses statistical monitoring of key performance indicators (KPIs) to monitor outliers. This may not provide the diagnosis of an issue, but it can identify faulty equipment for preventative maintenance.
To get a glimpse into the possibilities and generate ideas, we asked innovative, leading building analytic experts to contribute examples that would illustrate how other building systems can benefit from analytics. Here are their contributions.
Mike Welch of Control Network Solutions states the key to analytics for lighting systems is addressable networkable light fixtures communicating with an open standard protocol such as DALI® or IEC 62386. This allows the acquisition of data and command points which can develop into “value Information”. This value information for standard lamp fixtures can be as simple as:
• LED driver/Lamp Ballast failure
• LED overvoltage
• Mains Failure
• DALI network failure
The more complex reporting would be:
• Calculated power per fitting
• Dirty lamp fitting versus lamp fitting failing
• General lamp fixture failure
• Lamp change versus energy consumption cost analysis
There are also analytic opportunities with Emergency lamp fixtures. These fixtures typically are a lamp and rechargeable battery pack unit and need to be tested regularly by law in most jurisdictions. While these tests are carried out mostly manually, networkable emergency fixtures can be fully automated from initiating the tests to recording results and sending alarms when failures occur. The data that can be acquired includes:
WATER USAGE DATA
John Petze of
provides a recent example where analytic rules were applied to water
usage data to identify elevated water use during known unoccupied
periods, which could be attributed to a leak. Analytic rules were
also used to find periods of time when peak usage was too high. In
addition, rules were written to calculate the cost of the water usage
during periods of leaks and the cost associated with usage above
expected consumption levels.
Another example provided by John Petze is the utilization of raw energy consumption data supplied by a utility company. On a daily basis interval meter data (KW and KWh) is supplied in a CSV file format. Using analytics it is possible to identify buildings operating outside of their occupancy schedule simply by analyzing the “signature” of KW demand. Buildings that follow an occupancy schedule see a notable increase (and decrease) in KW within a short window of time and this represents occupancy transition times. Buildings where systems operate continuously do not see that step change. Adjusting for the magnitude of the change at the occupancy transition provides “filtering” to detect lack of schedule control even in cases where there are systems that operate continuously.
CORRELATION OF EQUIPMENT SERVICE DATA
In this example, John Petze explains that analytics were used to correlate service/repair events with equipment and identify the equipment that was responsible for high levels of service calls and related costs. Fairly simple data such as service dates, equipment subject to service and service cost or hours allowed for initial targeting of costly units. Additional information such as type of repair (based on a defined set of classification names) allowed for understanding of the types, frequency and costs of different issues.
WATER SYSTEM AND CONVEYANCE EQUIPMENT
Andres Szmulewicz of Smart Buildings LLC contributed several examples of useful analytics for water and conveyance systems.
A useful analytic for water distribution or irrigation is the monitoring of power usage and gallons per minute (gpm) of the pumps. If the kw/gallon increases in comparison to the historical data for the same gpm (i.e. not just a performance curve variance), you may assume that the pump (while operational) needs servicing because it has become inefficient.
For conveyance equipment, monitoring of the weight load sensor, drive power consumption, and travel distance can determine the status of the motors and whether they are working beyond capacity. For instance, if the kw/ft.*lb increase in comparison to historical data, you may assume the motor (again, while operational) requires service because it is becoming inefficient.
POWER MANAGEMENT SYSTEMS
Turner of Controlco states there are several other systems in the
building envelope that benefit from analytics and fault detection when
they are integrated into the same architecture. Lighting controls and
power management are the next logical systems to include fault
detection. While these systems already include some level of fault
detection, there is a strong argument to be made that including these
faults within the context of the building is a better solution.
to Turner, lighting control systems often include fault and
system alarms for the devices and controllers. They rarely have
algorithms defined to measure how well the sequences are performing and
if the overrides are impacting the overall energy performance of the
building. Similar to HVAC, lighting sequences and control strategies
are customized for the building and its occupants. It is simply not
possible for the manufacturer to pre-determine what analytics or fault
detection algorithms to include in the system before the sequences are
commissioned. Once the sequences are understood, rules can be created
that will measure the effectiveness of the sequence as compared to
occupancy, HVAC schedules, energy goals, safety and other related and
non-related data sets.
management is another area to consider for fault detection.
Similar to lighting control, the power management system rules cannot
be built until the building sequences for HVAC and lighting are
implemented and time has passed to build a baseline of information.
Energy managers largely look at the energy consumption in reports,
either through dashboards or spreadsheets, to determine if they are
meeting their energy goals. Analytics and fault detection can be
applied to look for the same anomalies the energy managers look for,
but they do it near real time and inform the appropriate energy manager
of the situation. If the proper systems are in place, additional rules
can be written to automatically take corrective action and avoid new
demand peaks or energy records.
Jim Lee of Cimetrics tackles IT Infrastructure, stating that many organizations have an IT infrastructure that is both centralized (data centers) and distributed (telecommunications/server rooms). Although energy efficiency is sometimes considered, especially during construction, the real value propositions that matter to the IT department are reliability and risk mitigation (ensuring uptime). The reliability of the infrastructure depends on a number of factors including environmental conditions, power quality, and IT equipment performance. Fault detection and diagnostics (FDD) can be applied to the reliability problem, but it requires the fusion of data from disparate sources such as HVAC, power metering, electrical switchgear, uninterruptable power supplies, backup generators, servers, routers and switches. Data is collected via SNMP, Modbus, BACnet, and proprietary protocols. According to Lee, today's servers can provide management data, including internal temperatures and computational workload metrics, which are useful inputs to a FDD system. For IT, the benefits of FDD are to predict equipment failures before they occur and to provide insight into the cause of failures that do occur. This requires that FDD analytics operate at the system level as well as at the equipment level.
DEMAND RESPONSE AND REFRIGERATION
Brian Thompson and
Chuck Sloup of Ezenics provided their insight and
analytic expertise into demand response (DR) and refrigeration.
According to the folks at Ezenics, one of the benefits of near real
time analytics is the ability to determine if a demand reduction
program has met its bid/goals. If the customer finds themselves in a
situation where they are not meeting bid/goals, analytics will be able
to tell why goals were not met in order to make changes for the next DR
event to be successful. For this to happen, all building systems
that consume energy need to be monitored for correct operation and
coordinated in a central location. For instance monitoring the
operating modes of a lighting control system in a retail establishment
will determine if the system is “ready” for an automated DR event such
as shutting off certain lighting tracks in order to reduce load. This
automated load reduction cannot be achieved if the lighting circuit HOA
switch has been overridden to the hand position. Therefore the
analytics will send a notice to the facility manager to make the
correction so that the system is “ready”.
Another benefit of analytics is the ability to monitor systems that are an integral part of the client delivering their product or service to their end customers. For instance, monitoring the refrigeration system in a grocery/convenience store will determine if the system is working at peak efficiency and effectiveness thereby preventing product loss due to spoilage. The criteria for peak efficiency and effectiveness are typically defined by the refrigeration case manufacturer. A typical requirement is to achieve a minimum number of defrost cycles per day to determine if the evaporator coil can achieve maximum flow, thereby protecting product from frost buildup.
Analytic software is a relative new class of tools for building owners and managers, providing real-time analysis and diagnostics of multiple systems and really adding some “smarts” to the building. As the examples provided by our guest contributors, it’s not difficult to imagine analytic tools for other building systems and the potential for enhanced intelligence built into tools for facility management.
Smart Buildings would like to extend a gracious thank you for the contributions made to this article.
Lee of Cimetrics – www.cimetrics.com
John Petze of Skyfoundry – www.skyfoundry.com
Andres Szmulewicz of Smart Buildings – www.smart-buildngs.com
Brian Thompson and Chuck Sloup of Ezenics – www.ezenics.com
Brian Turner, Controlco – www.controlco.com
Mike Welch of Control Network Solutions - www.control-network-solutions.co.uk
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