SRP will share how its analytics team uses AMI data and predictive modeling to improve participation and performance in its residential demand response program, which currently supports more than 100,000 enrolled thermostats and over 120 MW of dispatchable energy. Attendees will learn how SRP combined meter data, customer behavior patterns, and predictive analytics to identify customers most likely to contribute meaningful demand response savings. The session will focus on practical modeling approaches, program targeting strategies, and how analytics can improve customer recruitment and overall program effectiveness.

Session Takeaways:
• How AMI data supports predictive analytics
• Ways to use customer behavior in modeling
• Practical examples of improving program targeting
• Benefits of predictive analytics for demand response