Classification of electricity customer groups towards individualized price scheme design

This paper introduces classification of electricity residential customers into different groups associated with individualized electricity price schemes, such as time-of-use (TOU) or critical peak pricing (CPP). We use an unsupervised learning method, K-means, assisted by a dimensionality reduction technique and an innovative supervised learning method, extreme learning machine (ELM), to cluster daily load profiles based on hourly AMI measurements. Then, the achieved typical daily load profiles are analyzed and utilized for the design of an electricity price scheme for every subgroup based on symbolic aggregate approximation (SAX). These carefully designed and customized retail price schemes can provide a potential tool for price-based and incentive-based demand response in the Smart Grid context.