Alabama Power is developing advanced data analytics and data science techniques, including machine learning and predictive modeling, to anticipate electric vehicle (EV) charging behaviors with unprecedented accuracy.

In the era of rapid electrification, understanding and accurately predicting EV charging demand is paramount for efficient grid management and sustainable energy usage.

Alabama Power explains their inspired use of Advanced Metering Infrastructure (AMI) data for EV prediction. AMI data, with its high granularity and real-time monitoring capabilities, provides a treasure trove of information for identifying EV charging patterns realizing tangible benefits for utilities, rewards program participants, and EV owners.

Utility professionals joining this session with Alabama Power will:

• Learn about the key variables influencing EV charging demand.
• See real-life examples of successful implementations of AMI data analytics in EV prediction, emphasizing its role in grid planning.
• Identify how marketing teams can use insights from AMI data to design targeted incentives and personalized offerings for EV owners.
• Explore the specifics of data preprocessing, feature engineering, and model selection, highlighting best practices and potential challenges.