In Analytics, Use Cases

Sixteen member states of the European Union are currently deploying smart electricity meters. Five member states are deploying smart natural gas meters.  According to a European Commission report, by 2020, 72 percent of meters across the member states will be smart meters.

2020 is still five years away, and the European Commission had originally targeted 80 percent penetration of smart meters by 2020. Shareholders and regulators don’t want to wait years before seeing a return on the investment in smart meters.

If a country is just starting to roll out smart meters, where should they put their first 20 percent of meters to start realizing benefit? Answering that question demands situational intelligence.

Situational intelligence incorporates spatial, temporal and nodal dimensions into analytics. Spatial and nodal concerns for prioritizing smart meter deployments include

  • Where in the service territory does the meter stand (including proximity to other meters to deploy at the same time (route optimization)?
  • Where on the distribution network does the meter lie (network relationship)?
  • What is the age and type of building associated with the meter?
  • What electricity or gas usage is associated with that meter and building?
  • Is the location, network relationship, building type and usage representative of a class of customer or usage that you might want to study (population sampling)?

Once deployed, a small, early subset of smart meters can provide a rich new data source for other applications such as distribution optimization, demand response, energy efficiency program design, revenue protection and more.

In deploying smart meters, situational intelligence and other analytics projects, it pays to begin with the end in mind. You’re less likely to lose your way and more likely to start realizing returns on your investment.

Recommended Posts

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.