Many energy and utility companies get their first introduction to big data after they implement a smart meter project. While traditional meter reading occurs once a month, a smart meter can stream data every 15 minutes, or 2,880 times a month. That’s a whopping 287,900 percent increase in data that must be managed. This volume of information makes meter data a big data issue.
The early interest that utilities had in smart meters was to use the data in the meter-to-cash process. Utilities with sufficiently mature installations are now moving beyond meter-to-cash and are starting to explore how to turn meter data into insights about operations and customers. By applying analytics, utilities are learning that meters are more than just billing devices – they are windows into operations and customer behavior.
For example, one of the most common causes of power outages is the failure of transformers, many of which aren’t instrumented. To better understand which transformers are most likely to fail, utility companies can use meter data to recreate the loads over time on individual transformers. By understanding the loads being carried by individual transformers, utility companies can take actions that will help eliminate future failures. In the short term, overloaded transformers can be replaced or upgraded. In the longer term, additional investment can be made to add capacity to those portions of the distribution network that are consistently operating beyond planned capacity. The result is reduction in outages, better use of maintenance resources, and ultimately, improved customer satisfaction.
While managing the volume of smart meter data can present a challenge, the insights it provides through analytics can help reduce costs and increase client satisfaction.
Watch the short video above to learn how a new offering from IBM and eMeter, a Siemens business, is helping utilities improve distribution planning, prevent outages, engage consumers and increase profitability.