On average, meter readers, for example, once collected one reading per customer per month. Today, utilities have access to an almost overwhelming amount of data from both meters and other smart endpoints on their infrastructure, as well as external sources such as news and weather aggregators. To realize maximum value of all the data their communication system delivers, utilities need data analytics.
The first thing utilities should understand when adopting data analytics is that the majority of these applications are communication vendor agnostic. However, a fixed-base communication network with dedicated spectrum and the ability to prioritize incoming data is more efficient and reliable.
Data was king at the Utility Analytics Summit I recently attended where utilities shared some of their biggest challenges, including:
• How do I collect data?
• How do I analyze data?
• How do I turn data into actionable insights to improve operations, reduce cost and enhance customer service?
One panel at the Summit, hosted by Sensus Executive Vice Presidents Randolph Wheatley and John Stafford, focused on “Evolving Your Data from Advanced Metering Infrastructure (AMI) to the Enterprise.” Panelists included Diane McBeth, AMI and meter data management operations manager at Southern Company, and Brian Crow, CEO at Verdeeco, a smart grid analytics company recently acquired by Sensus. The panel discussion took attendees beyond the ‘how’ of retrieving and analyzing data to the ‘why’ of turning data into actionable intelligence.
How do I collect data?
“Utilities and their customers are thirsty for basic data analytics, business intelligence and visualization,” said Crow. “With a holistic approach to analytics solutions, utilities can quench this thirst and realize an even greater return on their communications infrastructure.”
Communication networks provide data such as customer usage, but utilities should also consider what other sources of data exist inside their systems that should be used to ensure a big picture view. For example, Crow discussed how data collection enables walls to come down between different departments at a utility. While departments like customer service traditionally had limited interaction with departments such as operations, data collection and analysis enable every department to contribute to the big picture. The actions of one department often affect the entire utility and data analysis showcases this. “If you build it, [utility departments] will come and the walls that have separated them will come down,” said Crow regarding data collection and analytics.
How do I analyze data?
“Data tends to create more data. Data analytics creates more analytics,” said Crow. For instance, if a utility has information on customer usage and information on daily temperatures, the utility can analyze the specific relationship at each household on customer usage. Through this data analysis, you have created a whole new set of data. This type of data analysis is often useful when answering customer questions, but the real challenge lies in transforming all of the data into useful and discernable information for the utility.
Utilities must analyze data to turn it into this useful intelligence. To analyze data, utilities have two main options: 1) Build a system in-house or 2) Source an outside vendor. McBeth discussed the benefits and challenges associated with building a system in-house. Some of the benefits include utility-specific customization and the realization of operational savings more quickly, which ultimately benefit customers. However, McBeth noted there are also challenges to building a system in-house. “Building an in-house system requires the right expertise and resources, both to build it and for ongoing support and maintenance. When analytics span across multiple operating companies, functions, and/or business units, it requires an understanding of the various regulatory and business requirements as well as buy-in across the organization. Venders provide solutions for utilities that do not have the option to develop their own in-house solution,” said McBeth.
Crow provided another perspective by sharing the benefits and challenges of working with a data analytics vendor. “Data analytics is an evolving space, and it can be difficult to keep up with the trends,” said Crow. “Utilities, particularly IOUs, have internal constraints to work through and often cannot take the risks required to innovate. Vendors are able to offload the risk associated with R&D much more efficiently.”
How do I turn data into actionable insights?
A key part of choosing how to analyze data is to determine what data is required to best improve operations, reduce costs and enhance customer service.
One key example Crow cited is a utility with a failed transformer. Prior to data analytics, the utility automatically would install a larger transformer, assuming the previous transformer failed due to its load. One of the data analytics applications that Verdeeco and Sensus offer determined that the transformer did not fail due to demand and was in fact over-sized. Based on this data, the utility was able replace the transformer with the appropriate size. Many utilities are even able to downsize their transformers on a broad scale. The transformer utilization application also enhances customer service for utilities, preventing customers from losing power in an unscheduled outage by predicting potential transformer failure. “Transformers never fail at the time most beneficial for the utility to replace, but tend to fail at the most expensive time of day and largest impact to the customer,” said Crow.
Another significant benefit of data analytics is revenue forecasting. With the ability to bring in meter data every fifteen minutes, instead of once a month or more, utilities can track their earnings in real time.
McBeth discussed specific benefits of data analytics for Southern Company. Some examples included daily usage data, which provides additional intelligence to better manage the business. McBeth also discussed the benefit of data analytics for Alabama Power during the 2011 tornadoes that hit Tuscaloosa. The meter outage data combined with the data from its outage management system created efficiencies with the restoration process, including staging the crews and material. AMI data contributed significantly to the fast response and successful restoration efforts.
Every utility has unique challenges, but the solution lies in data for many. With the right data analytics solution, utilities can manage the data and, most importantly, use it to improve their utility and the customer experience.
Dale Harber is executive vice president of global marketing at Sensus. This story originally appeared on Intelligent Utility and has been republished with permission.
