In just a little more than a week, the Utility Analytics Institute team will be packing our roller bags, pinning "Utility Analytics Week or bust" signs to them, and heading to Atlanta for Utility Analytics Week
. I thought it would be fun to preview a key topic we'll be covering at the event by pulling an analytics story from our Utility Analytics Institute project profile archives. This story dives into the world of smart meter data analytics, which is something that we'll be discussing throughout the event.
This utility is undertaking an effort to optimize the value of its smart meter data. The project includes use cases that touch on a variety of functional areas. Through this initiative, the utility hopes to provide situational awareness, reduce the time required for operators to investigate an exception, and enhance overall operational efficiency through the application of meter data analytics. The primary business driver behind this initiative was the opportunity to significantly increase operational efficiency.
The investment in network management, operations and data analytics required to make this project a reality is estimated at $17 million, which does not include investments previously made by the utility in its initial smart meter initiatives.
The utility is also in the process of building an enterprise-wide solution that will provide the foundation needed to support a variety of analytics projects and applications. A roadmap for this project is currently being developed. This roadmap will define the utility's vision for the platform and will include guiding principles.
Future analytics applications are expected to rely on the enterprise-wide solution including: campaign management, transmission and distribution management, transformer management and substation equipment management.
The primary technology components supporting this initiative include:
- Smart meter network management system
- Utilization software package
- Analytics appliances to support data analytics
- Approximately 11 servers
- Three databases
- Data storage devices
The project calls for data from eleven disparate data sources to flow through an ETL (extract, transform and load) tool into a large storage database. Analytics will be run against that data and will be pushed out into a visualization database. The visualization database will support the graphical display of the information through a user interface that sits on top of the smart meter network management system.
A number of business processes are expected to be impacted, including:
- Meter exception management | The process of troubleshooting problem meters is expected to become significantly more efficient. The utility identifies 10,000 exception meters per day. A room full of people access eleven different data sources and look meter-by-meter to try to discover the underlying issue. This project is expected to automate this entire process and allow resources to focus on resolving - versus finding -- meter problems.
- Network management | Completion of this project will provide insight into holes and gaps in the utility's mesh network and enable corrective action. The utility will gain a more complete understanding of the unique characteristics of its network, and better network design and engineering decisions will be the result. The utility is building a self-learning model that will track and trend faults and failures, predict equipment failures, and so forth. The utility expects an overall improvement in its asset management capabilities.
The ability to accurately identify meter errors and take appropriate action will deliver significant benefits. Truck rolls will occur only when necessary and will be based on a clear understanding of the nature of the problem. Preventative maintenance will be enabled by the ability to predict equipment failures. The impact on the utility's business processes is anticipated to be substantial.
Collaboration is occurring across a number of different business functions, even beyond those areas thought to be impacted. Groups ranging from distribution operations to marketing are engaged and collaborating in an effort to extract the maximum value from the utility's smart meter data.
ROI has been calculated for this effort and a 13- to 15-year payback is projected.
Each use case will have its own set of metrics. At the very least, this project will make it possible to create a baseline against which future results can be measured. Some initial metrics, such as cycle time and expected efficiency, have been established though it is too early in the project to validate these targets. The project team expects to develop key performance indicators (KPIs) to measure the benefits realized. Since projected cost savings are immediately removed from operational budgets, it is essential that estimated cost savings be accurate and well-supported.
This project is in perfect alignment with the utility's corporate strategy. In fact, a major focus for 2013 is to be "innovative and efficient," and this project directly supports that objective.
Strategic analytics-centric goals have not really been spelled out. The utility is committed to using technology to become a leader in innovation within the utility industry, and analytics will play a major role in achieving that objective.
The title of the technology lead for this initiative is an IT lead, the title of the business-side project manager is the smart meter operations project manager, and the title of the executive sponsor is the vice president of customer services. The IT lead, the smart meter operations project manager and a business analyst have been dedicated to this project on a full-time basis.
The utility expects to engage up to 11 internal resources for a short period of time. During this period, some of these resources will be dedicated to this project on a full-time basis, some may be contracted resources and some resources may be part time. The majority of these resources will be IT resources since the project does not fund business-side resources. Business-side resources will be interviewed, some extensively, and involved as subject matter expertise is needed.
External providers have provided valuable input into the overall project strategy, architectural design, and business case development (e.g. cost and time estimates). External providers will be providing professional services to support implementation as well as ongoing support of the solution.
So what is your utility doing with meter data analytics? What challenges and opportunities are you facing? I'd encourage to check out some of our resources for meter data analytics, including conversations at Utility Analytics Week
, as well as our meter data analytics working group
and a recent report
on the topic. Or feel free to give me a holler and we'll help you find the information you need.
Thanks for reading!
H. Christine Richards is the research director for the Utility Analytics Institute.