Customer sampling in a smart grid pilot

Abstract

Smart grid pilot projects require a representative subset of the total population to draw relevant conclusions from test results. However, customers willing to participate in such projects are not always representative to the whole population. Standard random sampling gives some problems because not all results can be scaled. Defining sub-populations or strata to random samples from is theoretically sound, but the definition of sub-populations is quite expensive. The paper presents a customer sampling technique based on quota. The domains for the quota are defined by machine learning algorithms and the quota themselves are based on realistic data. Sampling is done by an optimization algorithm, which eliminates the common `human error'-factor in quota sampling. The approach is a cost efficient and convenient way of sampling that is able to balance the representativeness of the electricity consumption patterns for the population against sampling accuracy. The method has been applied and validated on a large customer data set.