Networked and Embedded Systems Laboratory

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A key prerequisite for residential energy conservation is knowing when
and where energy is being spent. Unfortunately, the current
generation of energy reporting devices only
provide partial and coarse grained information or require
expensive professional installation. This limitation is because of 
the presumption that calculating per-appliance consumption
requires per-appliance current measurements. 
However, since appliances typically emit measurable signals when 
they are consuming energy, we can estimate their consumption using indirect sensors. 
This paper presents ViridiScope, a fine-grained power monitoring
system that furnishes users with an economical, easy-to-install,
self-calibrating tool that provides power consumption of virtually
every appliance in the home. ViridiScope uses ambient signals from
inexpensive sensors near appliances to estimate power consumption, thus
no in-line sensor is necessary. We use a model-based
machine learning algorithm that automates the sensor calibration
process. Through experiments in a real house, we show that ViridiScope
can estimate the end-point power consumption to within 10% error.

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