Smart meter deployments as the foundation for Smart Grid are now gaining in momentum, scale and maturity. In 2011, Smart meter penetration in North America was estimated to be more than 45% and predicted to be over 60% by 2016, and other geographies are gearing up to progress beyond the toe-in-the-water pilot projects. The recent government mandates in France and Spain committing their utilities to smart meter deployments is clear evidence of this. These challenging rollouts, however, will be well beyond the scale of the largest American deployments.
The fundamental utility Meter to Cash challenge has now been pretty well addressed by implementing the most appropriate MDMS (Meter Data Management System) solution. This has not been without some mishaps and false starts as some companies switched out some or all of their MDMS technology to overcome performance, scalability or integration issues. Challenges have ranged from switching to a completely different MDMS provider to changing the underlying database of a solution to one more optimized for the task, such as IBM Informix Time Series.
I find it interesting to note that despite the clear proof points demonstrated in successful North American implementations, there is still a tendency to eschew a packaged approach to the Meter Data Management challenge. “Roll-Your-Own” MDMS seems to be the preferred approach of some of the large European utilities; it’ll be interesting to gauge their progress.
I recently read in IDC’s 2012 Predictions for North American Utilities that there would be significant growth in Utilities spending in analytics in anticipation of Big Data to support Grid Operations, Customer Service and Trading activities. This prediction is certainly holding true as we see ever increasing interest in analytics as a method to gain business benefit from the sudden growth in the volume of usage data. However, it is interesting to note that utility meter usage and grid event data - whilst representing a significant change in the data volumes that utilities are required to manage - are not “So Big” by other industry benchmarks. Utilities can learn a lot about managing volume data from those other industries as many companies have already recognized the need for an optimized analytical platform in the same way that utilities are accepting the requirement for an optimized platform to underpin their MDMS.
A great example of this is the recent collaboration between IBM and eMeter. In 2011 eMeter extended their EnergyIP suite with the introduction of their Analytical Foundation However, to address the expanding analytical requirements of their largest customers, eMeter recognized the need for a high performance analytical appliance which combined speed, scalability, simplicity of use and a smarter way to develop analytics. This provides the basis for a whole range of analytical applications ranging from fraud detection to transformer load analysis all achieved by harnessing the meter usage data. For more insight into the eMeter Analytical Foundation watch the on demand webinar http://bit.ly/Lc1wKr. Adding further context to this data in the form of customer and asset management data will enable utilities to better service their customers and manage the whole grid effectively.
As my colleague Dave Shipman mentioned in his recent blog on Smarter Analytics for E&U we are only scratching the surface of the analytical potential becoming available to the industry. Once the essential KPIs and dashboards are in place the appetite for Predictive and Prescriptive Analytics will increase. Streaming analytics will be introduced to better sense-and-react to grid events and utilities will begin to tap into the whole area of unstructured data. A great example of such streaming analytics is the DoE sponsored Smart Grid Demonstration project at PNNL, for details check out the recent Twitter Blog http://ibm.co/MIOYb1.
I’m also working with a number of companies looking to harness Social Media Data around Sentiment Analysis and am aware of others undertaking projects in Machine Log Analysis.
I look forward to providing some further insight and commentary in future blogs. For more information on this subject, I invite you to read and respond to the new IBM paper Managing big data for smart grids and smart meters .
Disclaimer: The postings on this site are my own and don't necessarily represent IBM's positions, strategies or opinions