Smart Metering Load Data Compression Based on Load Feature Identification

Abstract

In recent years, smart meters have been widely installed in households across the world, which has led to problems with big data. The huge amount of household load data requires highly efficient data compression techniques to reduce the great burden on data transmittance, storage, processing, application, etc. This paper proposes the generalized extreme value distribution characteristic for household load data and then utilizes it to identify load features including load states and load events. Finally, a highly efficient lossy data compression format is designed to store key information of load features. The proposed feature-based load data compression method can support highly efficient load data compression with little reconstruction error and simultaneously provide load feature information directly for application. A case study based on the Irish Smart Metering Trial Data validates the high performance of this new approach, including in-depth comparisons with the state-of-art load data compression methods.