Thermal Smart Plants: Thermal Sensing for Non-Intrusive Device Energy Disaggregation

We contribute an innovative and non-intrusive method for disaggregating the energy load of different devices using thermal and environmental sensors embedded in plant containers. Indoor plants are ubiquitous, and obtaining containers with sensors that monitor growth conditions is becoming increasingly common. Our work harnesses these developments, providing a non-intrusive and real-time monitoring system that uses residual thermal radiation to identify nearby appliances and disaggregate them. Proof-of-concept experiments considering devices with high and low energy profiles demonstrate that our approach can accurately identify electric appliances and support the disaggregation of power measurements for more accurate load monitoring.

Authors

Institutions

Publication Details

Published
2025-10-14
DOI
https://doi.org/10.1145/3737905.3769283
Primary Topic
Green IT and Sustainability
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Thermal Smart Plants: Thermal Sensing for Non-Intrusive Device Energy Disaggregation

Fatemeh Sarhaddi, Naser Hossein Motlagh, Marko Radeta, Ngoc Thi Nguyen et al.
Green IT and Sustainability
article

Thermal Smart Plants: Thermal Sensing for Non-Intrusive Device Energy Disaggregation

Fatemeh Sarhaddi, Naser Hossein Motlagh, Marko Radeta, Ngoc Thi Nguyen, Zahra Safaei, Agustin Zuniga, Sasu Tarkoma, Petteri Nurmi, Huber Flores, Yangyang Wang, M. Fiaz Khan
article en

Abstract

We contribute an innovative and non-intrusive method for disaggregating the energy load of different devices using thermal and environmental sensors embedded in plant containers. Indoor plants are ubiquitous, and obtaining containers with sensors that monitor growth conditions is becoming increasingly common. Our work harnesses these developments, providing a non-intrusive and real-time monitoring system that uses residual thermal radiation to identify nearby appliances and disaggregate them. Proof-of-concept experiments considering devices with high and low energy profiles demonstrate that our approach can accurately identify electric appliances and support the disaggregation of power measurements for more accurate load monitoring.

University of Helsinki (FI), Helsinki Art Museum (FI), University of Tartu (EE), Universidade da Madeira (PT)
Openalex Percentile: Top 71%
Green IT and Sustainability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.