PEAT, how much am I burning?

S. N.Akshay Uttama Nambi, R. Venkatesha Prasad, Antonio Reyes Lua, Luis Gonzalez

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)


Depletion of fossil fuel and the ever-increasing need for energy in residential and commercial buildings have triggered in-depth research on many energy saving and energy monitoring mechanisms. Currently, users are only aware of their overall energy consumption and its cost in a shared space. Due to the lack of information on individual energy consumption, users are not being able to fine-tune their energy usage. Further, even-splitting of energy cost in shared spaces does not help in creating awareness. With the advent of the Internet of Things (IoT) and wearable devices, apportioning of the total energy consumption of a household to individual occupants can be achieved to create awareness and consequently promoting sustainable energy usage. However, providing personalized energy consumption information in real-time is a challenging task due to the need for collection of fine-grained information at various levels. Particularly, identifying the user(s) utilizing an appliance in a shared space is a hard problem. The reason being, there are no comprehensive means of collecting accurate personalized energy consumption information. In this paper we present the Personalized Energy Apportioning Toolkit (PEAT) to accurately apportion total energy consumption to individual occupants in shared spaces. Apart from performing energy disaggregation, PEAT combines data from IoT devices such as smartphones and smartwatches of occupants to obtain fine-grained information, such as their location and activities. PEAT estimates energy footprint of individuals by modeling the association between the appliances and occupants in the household. We propose several accuracy metrics to study the performance of our toolkit. PEAT was exhaustively evaluated and validated in two multi-occupant households. PEAT achieves 90% energy apportioning accuracy using only the location information of the occupants. Furthermore, the energy apportioning accuracy is around 95% when both location and activity information is available.

Original languageEnglish
Title of host publicationProceedings of the 9th ACM Multimedia Systems Conference, MMSys 2018
EditorsPablo Cesar
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
ISBN (Electronic)978-1-4503-5192-8
Publication statusPublished - 2018
Event9th ACM Multimedia Systems Conference, MMSys 2018 - Amsterdam, Netherlands
Duration: 12 Jun 201815 Jun 2018


Conference9th ACM Multimedia Systems Conference, MMSys 2018


  • Energy apportioning
  • IoT
  • NILM
  • Shared spaces

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