AEP Sustainability - Data Analytics

Data Analytics

Technology advancements in analytics are making things possible today that were unimaginable not so long ago. At AEP, we are using these advancements to help position ourselves as the energy company of the future.

We are advancing the use of data and analytics to solve problems, optimize processes and discover new business opportunities. For example, we completed a strategic segmentation of our residential customers to help us better understand what they need and expect of AEP. This gives us important information as we design new programs and services.

Within grid operations, several initiatives have been completed that provide monitoring, prediction and optimization capabilities that we didn’t have before. These efforts enable increased safety, reliability and customer value.

Examples of data and advanced analytics initiatives:

  • Microgrid analytics leverages internal and external data to optimize the siting of distribution microgrid/distributed energy resources. This reduces the amount of time needed to conduct site research and provides innovative partnership opportunities with the Enterprise Innovation and Charge organizations.
  • In 2018, we developed an analytics tool to automatically generate a list of transmission meter points requiring investigation. It is critical that meters are accurate to ensure more accurate bills for customers.
  • In 2018, we continued the development of a new tool to help automate the classification of some network faults on the grid. Where there’s an outage on the transmission grid, our dispatchers are expected to make a determination of what caused it. The classification of network faults can be a time-consuming process that in some cases requires a physical inspection to confirm the problem before repairs can be made. This tool allows us to target our outage response more accurately, saving time and money and enhancing the customer experience.
  • In 2017, a data analytics team was established to support both distribution- and customer-related needs. On the customer side, the focus was on customer segmentation and propensity modeling to help identify potential new service offerings. For distribution, the focus is on improving operational efficiencies and driving more informed business decisions.
  • We are planning to build our text analytics capabilities to automate document searches in our system. For example, our Enterprise Risk Management group is examining how to automate the review of current and historical damage or insurance claims. The result will be faster claim resolution for customers at a reduced operational cost for AEP.

To learn more about process automation, visit The Future of Work. As we continue to learn and advance in this space, we are beginning to focus on cognitive analytics to enable us to make recommendations to our customers based on their interactions with us. Currently, our customer interactions are largely transactional, such as paying a bill or turning power on and off. Having this new functionality will give us more information to better serve our customers, based on their usage and interests.