GRATE: Granular Recovery of Aggregated Tensor Data by Example

TL;DR

GRATE is a novel tensor factorization method that accurately disaggregates aggregated data into detailed components, demonstrated on energy consumption datasets, outperforming existing methods.

Contribution

The paper introduces GRATE, a new constrained tensor factorization approach for disaggregating aggregated data using examples, applicable to energy and other domains.

Findings

GRATE outperforms state-of-the-art energy disaggregation methods.

It effectively handles both exact and inexact aggregated data.

Experiments confirm improved accuracy in real datasets.

Abstract

In this paper, we address the challenge of recovering an accurate breakdown of aggregated tensor data using disaggregation examples. This problem is motivated by several applications. For example, given the breakdown of energy consumption at some homes, how can we disaggregate the total energy consumed during the same period at other homes? In order to address this challenge, we propose GRATE, a principled method that turns the ill-posed task at hand into a constrained tensor factorization problem. Then, this optimization problem is tackled using an alternating least-squares algorithm. GRATE has the ability to handle exact aggregated data as well as inexact aggregation where some unobserved quantities contribute to the aggregated data. Special emphasis is given to the energy disaggregation problem where the goal is to provide energy breakdown for consumers from their monthly aggregated consumption. Experiments on two real datasets show the efficacy of GRATE in recovering more accurate disaggregation than state-of-the-art energy disaggregation methods.