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Tag Your Way to Efficient Data Management

Tag, you’re it.

And in this case, it’s an extremely beneficial situation.

That’s because energy managers who oversee complex operations have an answer to one of the most common, time-consuming challenges — the need to analyze energy use from a variety of perspectives.

It used to be enough to understand total consumption and peak demand times. But that’s no longer the case. Same with looking at individual metering points and pieces of equipment.

To be truly effective, energy management requires context:

  • Aggregate use for heating, ventilation, and air-conditioning (HVAC) across buildings
  • Electric consumption specific to injection molding processes
  • Electric consumption for air compressor systems on site
  • Natural gas used by building, is split into heating and non-heating

Those are just a few of the almost endless examples.

Energy Data Management 4Makes sense on the surface, however, facility personnel that relies on a typical energy management system (EnMS) are often limited because of the way data is organized. If they choose to architect the system according to physical layout (i.e., building > area > equipment > meter), it becomes difficult to evaluate and compare all compressors in a single view. If they design the system according to equipment type, then it’s impossible to segment by area.

One of the age-old fixes is to allow users to organize their data in multiple hierarchies in the EnMS. The only problem: It’s still too restrictive. Every time energy data needs to be sliced in a new way, an operator has to create another hierarchy.

Enter tagging — a new, more streamlined approach that allows users to simply add context to the data points themselves. By defining tags such as “compressed air”, “injection molding” or “workshop 4”, it’s possible to evaluate and group data in any number of combinations without worrying about specific parent-child relationships that are inherent to data hierarchies.

Energy Data Management 1For instance, the Performance Analytics Module (PAM) in Resource Advisor includes features for tag-based aggregation and analysis. Once an energy manager creates a set of tags for electrical loads such as HVAC, lighting, and plugs, PAM automatically collects data for each usage type at all levels of the physical hierarchy and for all commodities metered in the system.

Lighting-specific consumption is immediately viewable with a single click, as a result, no matter how many different metering points there are at the site. Similarly, HVAC use can be displayed and then broken down by any location or process.

Reducing consumption and costs is an entirely different game from tagging. Finally, energy managers can view data, and identify and solve problems the way they want, free of system constraints.

For more information on energy data management, click here.