Aggregated functional data model applied on clustering and disaggregation of UK electrical load profiles

TL;DR

This paper introduces a novel methodology for disaggregating aggregated electrical load data into typical consumption curves and clustering substations based on these patterns, aiding energy demand analysis and planning.

Contribution

It presents a new functional data model that incorporates explanatory variables for load disaggregation and a model-based clustering approach for substations.

Findings

Effective disaggregation of substation loads into typical curves.

Successful clustering of substations based on consumption patterns.

Validated methodology on real UK data and simulated scenarios.

Abstract

Understanding electrical energy demand at the consumer level plays an important role in planning the distribution of electrical networks and offering of off-peak tariffs, but observing individual consumption patterns is still expensive. On the other hand, aggregated load curves are normally available at the substation level. The proposed methodology separates substation aggregated loads into estimated mean consumption curves, called typical curves, including information given by explanatory variables. In addition, a model-based clustering approach for substations is proposed based on the similarity of their consumers typical curves and covariance structures. The methodology is applied to a real substation load monitoring dataset from the United Kingdom and tested in eight simulated scenarios.