12/8/2015
 
Sponsored By:
Using Data to Improve Facility Operations
Author: John Petze, Principal, SkyFoundry


Perhaps we should start our discussion by saying that analytics is not "a thing". It's a valuable tool that can be used to measure and report the performance of things. We are all familiar with energy conservation measures associated with buying some type of equipment or device to install in our buildings, such as LED lighting or VFDs -- but analytics is very different. Let's highlight the ways that analytics-based efficiency measures are different from hardware-based efficiency measures, and how those differences should be considered as facility owners look at implementing an analytics program for their facilities.

It Starts With Data
With analytics, we are talking about using the data that comes from things -- the devices and equipment systems that are most likely already in our buildings. These include sensors, building control systems, meters, lighting control systems and the like. Analytics applies software technology to automatically analyze the data produced by these devices to identify patterns that represent issues, faults, deviations, and anomalies, all of which present opportunities for improved performance and reduced operating costs.

Identify Available Data
The first step in considering analytics is to identify what data is available from your existing investments. Many owners are surprised at how much data is available and the value that can be derived from it. Just because the data is not already flowing across your desk everyday does not mean it is not available. Here are a number of myths and misconceptions about data and analytics that often impede organizations in moving forward with an analytics program.

Myth #1: "You have to be able to connect to real-time data in order to benefit from analytics".

This simply isn't true. Being able to connect to live data from a building automation system, metering system, or other data source has benefits but is not necessary in order to get substantial benefits from analytics. We see many projects deliver substantial results using historical data such as interval meter data from electric meters, or historical data logs from a building automation system. These data logs can be exported from the system and then imported into an analytics platform. Once the historical data is loaded, the analytics platform quickly process rules and algorithms to identify issues, benchmark performance, and produce actionable reports on operational issues, with results identified in seconds or minutes.

For example, let's say the only data that can easily be accessed is interval meter data (kW demand and kWh consumption). This can be provided once per day by the utility company in a .csv file, along with a list of occupancy schedule times in a manually maintained spreadsheet. With just that limited amount of data, analytics can identify:
  • Buildings that are starting too early
  • Buildings that are running too late
  • Buildings that operate continuously (no schedule control)
  • Demand peaks that occur outside of occupied times
  • Peak load (annual & monthly) and short load durations
This is just one example which shows how tremendous value can be derived from very limited historical data. So while access to real-time data has benefits, don't let an inability to get real-time data dissuade you from moving forward with analytics. There are tremendous benefits to be achieved with data that is a day, a week, or even a month old.

Myth #2: "I will have to get every piece of data from every system and sensor in my building in order to benefit from analytics, and that's just too difficult a task".

While it's true that that the range of analytics that can be performed is related to the data available, that doesn't mean you need data from every system and device to get benefit from analytics. The goal of analytics is not to monitor, measure, and analyze everything. The goal should be to quickly generate financial returns by identifying operational issues with the most efficient investment of time and money. It's actually smart to begin with a smaller analytics project using easily available data, as in our example above. Proving results and generating financial returns quickly is the best way to justify going further with the deployment of analytics.

And let's not forget that analytics on their own do not save money. It's only by correcting the issues that have been identified by analytics that you save money and reduce costs. It makes sense to give your facilities teams the time to get comfortable with analytic results and mobilize the workflow process to prioritize and address findings.

Unlike most other efficiency-oriented investments, implementing analytics is an area where you can start small, prove results, and then continue forward. For example, you can't install half of a new high-efficiency chiller, but you can implement analytics in an incremental process and drive financial returns at every step. We see many highly successful projects start this way.

Myth 3: "Current analytic solutions won't 'go deep' into analysis of complex systems such as large chiller plants, unique HVAC designs, etc."

This myth represents the other side of the coin – the concern that the current state of the art can't handle sophisticated analytics for complex, unique systems, or the needs of extremely large portfolios. Here again, proven results show that this is not true.

While pre-programmed "standard" analytic rules can only go so far, user-programmable analytics allows rules and algorithms to be matches to the unique characteristics of individual buildings. The programmable approach enables building managers and facilities staffs to utilize their own specialized knowledge in conjunction with the deep expertise of external service providers (systems integrators, energy consultants, etc.) to define and implement analytics that fit the unique characteristics and requirements of individual facilities.

Being able to adapt analytics to the specific designs of individual facilities and owner needs is critical. The state of the art is most definitely at a point where analytics can be applied to even the most unique, one of a kind, complex facilities and equipment systems. As far as large portfolios are concerned, analytics can, and have been, successfully applied across large portfolios consisting of thousands of buildings and millions of square feet of space.

Myth 4: "In order to take advantage of analytics, I will have to install and maintain complex software and either train my staff to operate it, hire new people, or turn all of my data over to an external third party organization."

Today there are many options available from a range of analytic software and service providers. Owners that have strong, technically sophisticated, in-house facilities teams can choose to install analytics software on-premise, inside their secure network, and be directly involved in defining rules and actively working with the software on a daily basis. Other organizations may benefit from a consulting-oriented approach where a service provider operates and manages the software (typically in the cloud, but this can also be done on-premise), handles all rule development and refinement, and delivers detailed reports of findings along with recommendations and even services to implement corrective actions. This consulting-oriented approach is often referred to as “Analytics As a Service”. In summary, these examples demonstrate that analytics is not one thing. Analytics is a tool – an essential tool in today’s world where facilities are filled with a myriad of smart devices producing huge volumes of data. Analytics enables us to identify operational issues to reduce operating costs and improve overall performance, and there are options available to fit every type of organization and facility portfolio. Given the overwhelming business value of information and analytics, data really has become “the new money”.

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About Our Sponsor: SkyFoundry's SkySpark analytics platform automatically analyzes data from building automation systems, metering systems and other smart devices to identify issues, patterns, deviations, faults and opportunities for operational improvements and cost reduction. SkySpark is an open platform enabling data from a wide range of sources to be continuously analyzed, helping building owners and operators "find what matters" in the vast amount of data produced by their equipment systems.
 

 
 
 
 
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