Best Practices for Aggregating Energy Asset Information across the Enterprise

May 29, 2013 By Pablo Päster and Ellen Raynor

Pablo Päster and Ellen Raynor

Know your Six “C’s”

Every year company energy bills skyrocket during air conditioning season. The dramatic increase in energy costs and the increasing prevalence of time of day/variable pricing is compelling many companies to better understand where disproportionate amounts of energy are being used across their assets in all of their facilities.

Companies typically aggregate company-wide energy data from monthly utility bills at the building level. As companies mature their energy programs there is a desire to capture data associated with individual assets over more granular increments of time. Specific assets are often acquired at different times and for location or process-specific purposes, resulting in a diverse set of systems that may be difficult to network and benchmark against each other. For this reason, there are several considerations to make when planning to access energy data. The 6 C’s provide a best practice framework for preparing to access the data.


Each of the assets, whether they are heaters, chillers, or manufacturing equipment, have to be capable of collecting the desired information or an add-on technology may be required. In addition to having the ability to capture input, output, or other performance data, this capability needs to be enabled and configured to capture the data. Sometimes hard-wiring is required and, at other times it is a matter of finding someone to configure the equipment.


The device may need to be calibrated to collect accurate information. Factory settings of meters and data collection equipment may not be configured for the specific variables in end-use and may prove to be inaccurate or unreliable.


Each asset needs to be networked, either wired or wireless, to send out data. One plant visited by Hara had network-capable gas meters that were being captured on a clipboard twice a day and entered into a spreadsheet. Networking the meters yielded a three month payback period.


Meaningful analysis usually requires a longitudinal study achieved by collecting a sufficient time series of historical data. Many assets have no historian, so data is constantly being overwritten. The sooner data collection and archiving begins, the better. The amount of relevant historical data depends on the intended analysis. Analyzing a chiller’s relative performance at varying loads may require granular data, while correlating weather conditions (HDD/CDD) or production volume to energy use may require only bill-level monthly granularity.


Nobody wants to rely on data that they do not trust. They want current data that is complete and not subject to misinterpretation. The data set should not contain a long list of caveats or asterisks. In order to achieve this consistency, a protocol should be established to ensure that data is always transported in the same format at the same frequency and that there are mechanisms in place to flag changes.

The system used to review the data should perform validations to note any unexpected gaps or big changes that might indicate a failure in the collection system. Gaps are something to pay particular attention to–sensors that collect data may only note changes and a calculation may be required to convert the data into a continuous, evenly spaced, and accurate time series. The analysis system should also normalize data into a consistent unit of measure that allows for accurate consolidation.


Meaningful performance indicators are derived from business context, characteristics of the organizational structure, building types, the facility’s primary activities, facility age, construction details (masonry, steel, etc.), asset work output (number of computations in a data center, tons of cooling in a chiller, widgets produced by a production line, etc). Consider whether those normalizing characteristics can also be measured at a frequency that will yield meaningful analysis when combined.

Through the first four C’s–configuring, calibrating, connecting, and collecting –companies build up a repository of standardized data. This data reveals previously hidden insights and informs business decisions. While companies perform a parallel asset roll-up for benchmarking and analysis, they should still retain their building level bill roll-up for footprinting and external reporting. These two complementary and interconnected data collection approaches are the pillars of a holistic energy management approach.

Pablo Päster is senior director of account management and Ellen Raynor is SVP of Product for Hara Software.

Leave a reply