One of the most significant challenges with respect to “Big Data” applications today is the ability to “make sense” of the large volumes of incoming data. Most applications currently available focus on data “gathering”, and, particularly in the AMI space, often take the form of asset or inventory management tools. The true power of Big Data can only be realized if it can be transformed into new key performance indicators that tell something new about your business that you don’t already know, or couldn’t otherwise easily obtain elsewhere.
Another issue that often plagues Big Data applications is a lack of focus on the data with the highest potential. A good example that warrants more attention in the Smart Meter AMI vertical is events. Events in an AMI context can encompass anything from tamper and maintenance notifications to HAN/PAN/ZigBee data and associated network health. The volume of these events can be enormous, and new events are constantly being created as advancements in metering firmware and feature functionality increase.
Before these problems can be solved, and impactful KPI’s created, you first need to understand the data. That’s where standardization can help. In the Smart Grid AMI arena, I’m currently leading the development of the next edition of the IEC CIM 61968-9 standard. This standard includes a comprehensive rubric for categorizing AMI events and meter readings. Consisting of numerically assigned “four part identifiers” derived from an extensive list of categories, it provides the “building blocks” that enable any utility to immediately make sense of incoming data. Each event consists of a device indicator, a primary category and subcategory, and an action indicator. Relationships between such incoming data could be established based on the assigned CIM categories. For example, events in the security category could be juxtaposed against events within close proximity to identify threats.
Alarms or errors could be juxtaposed with change records and workforce management to establish failure patterns and better root cause analysis. Weather events could be combined with outage management and other weather related information to establish patterns over time that allow utilities to minimize and possibly even pre-empt service loss. The possibilities are endless. And since it is an integration standard, the CIM (Common Information Model) could be used to transfer this intelligence to other applications and systems throughout the enterprise.
Adoption of the IEC CIM standard is probably the best first step any utility can take in developing big data applications that provide truly impactful and unique KPI’s with the potential to transform their business.
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