Incentive Design and Utility Learning via Energy Disaggregation

Abstract

The utility company has many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company--consumer interaction as a principal--agent problem. We present an iterative algorithm for designing incentives while estimating the consumer's utility function. Incentives are designed using the aggregated as well as the disaggregated (device level) consumption data. We simulate the iterative control (incentive design) and estimation (utility learning and disaggregation) process for examples including the design of incentives based on the aggregate consumption data as well as the disaggregated consumption data.