Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for
observing power usage in buildings. It tackles several challenges in transitioning into a more …

This paper presents a floor-vibration-based step-level occupant-detection approach that
enables detection across different structures through model transfer. Detecting the …

A large share of the energy usage in buildings is driven by occupancy behavior. To minimize
this usage, it is important to gather accurate information about occupants' behavior and to …

Over the past few years, dozens of new techniques have been proposed for more accurate
energy disaggregation, but the jury is still out on whether these techniques can actually save …

Modern Internet of Things (IoT) applications, from contextual sensing to voice assistants, rely
on ML-based training and serving systems using pre-trained models to render predictions …

Buildings across the world contribute significantly to the overall energy consumption and are
thus stakeholders in grid operations. Towards the development of a smart grid, utilities and …

Occupant behavior determines a large share of the energy consumption of buildings.
Software applications driven by information about occupant behavior provide a mean to …

Energy disaggregation is a key enabling technology for residential power usage monitoring,
which benefits various applications such as carbon emission monitoring and human activity …

Non-intrusive load monitoring (NILM) is crucial to smart grid, which enables applications
such as energy conservation and human activity recognition. As a typical task of NILM …

Appliance recognition by a home energy management system (HEMS) is a principal
challenge in the area of future smart and home grid systems, particularly for electrical power …