Energy Disaggregation for SMEs using Recurrence Quantification Analysis

Laura Hattam, Danica Vukadinovic Greetham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Non-intrusive load monitoring (NILM) or energy disaggregation
aims to estimate appliance-level energy consumption from the
aggregate consumption data of households. While there is significant interest from academia and industry, NILM techniques are
still not adopted widely across households. This is mainly because
the techniques developed for one household cannot be generalized and applied in other households (applicability), and require
tremendous hand-tuning to apply across households (scalability).
To overcome the above issues, we propose a novel semi-supervised
energy disaggregation framework – UniversalNILM. The key idea
of UniversalNILM is to model appliances in a few training houses,
which has detailed appliance-level data and transfer this learning
on to test houses (blind disaggregation), which has only aggregate
house consumption to derive fine-grained appliance energy consumption. UniversalNILM was empirically evaluated across datasets
and outperforms the reported accuracy from both state-of-the-art
supervised and unsupervised NILM techniques.
Original languageEnglish
Title of host publicatione-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems
EditorsHartmut Schmeck, Veit Hagenmeyer
PublisherAssociation for Computing Machinery
Pages610-617
Number of pages7
ISBN (Print)978-1-4503-5767-8
DOIs
Publication statusPublished - 15 Jun 2018

Publication series

NameProceedings of the Ninth International Conference on Future Energy Systems
PublisherAssociation for Computing Machinery

Cite this

Hattam, L., & Greetham, D. V. (2018). Energy Disaggregation for SMEs using Recurrence Quantification Analysis. In H. Schmeck, & V. Hagenmeyer (Eds.), e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems (pp. 610-617). (Proceedings of the Ninth International Conference on Future Energy Systems). Association for Computing Machinery. https://doi.org/10.1145/3208903.3210280