Energy monitoring is one of the important aspects of the energy management, as such there
is a need to monitor the power consumption of a premises before planning some of the …

In the smart home context, one of the features that most users would desire is the knowledge
of the operation of each electrical appliance without the need to install expensive smart …

Many non-intrusive load monitoring (NILM) studies use high-frequency data to classify the
device's ON/OFF state. However, these approaches cannot be applied in real-world …

In this paper we evaluate several well-known and widely used machine learning algorithms
for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load …

In this paper, low-resolution smart metering data analysis is applied to breakdown the load
consumption for household appliances to facilitate the deployment of residential energy …

The Internet of Things (IoT) paradigm, in conjunction with the one of smart cities, is pursuing
toward the concept of smart buildings, ie,``intelligent''buildings able to receive data from a …

This study presents the experimental determination of the ZIP (constant impedance (Z),
constant current (I), constant power (P)) coefficients for residential appliances in a smart …

In this paper we evaluate several well-known and widely used machine learning algorithms
for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load …

The electrical appliances identification problem is gaining a rapidly growing interest these
past few years due to the recent need of this information in the new smart grid configuration …

Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power
consumption of individual appliances using the aggregate electricity consumption. Many …