Survey on- Behaviour of load profiling in big data

Abstract

The growing interest in discerning behaviors of electricity users in both the residential and commercial sectors
the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from
the computational viewpoint. This work uses both raw data from household consumption data and synthetic profiles. The
motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and
switching, fault and fraud detection, demand-side management, and energy efficiency measures.