Smart Meter Data Analytics for Optimal Customer Selection in Demand Response Programs

This paper describes a methodology to predict customers' eligibility to participate in Demand Response (DR) programs using real electricity data collected from customers over time by smart meters. These types of programs have been proposed to improve generation capacity as load demand increases and the two-way communications (between utilities and users) are enabled. Instead of installing new power plants in smart grids, utilities encourage users to shift their electricity consumption from peak hours to off-peak hours. The number of successfully recruited customers participating in demand response programs is usually low, and resources are wasted on recruitment efforts. The results of our research reflect that it is possible to predict with more than 90% accuracy which customers are good targets for DR program participation based on their consumption patterns and lifestyles. These data could ultimately improve the recruitment process for DR programs.