The rapid development of technology in the electrical energy sector within the last 20 years
has led to growing electric power needs through the increased number of electrical …

Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity
consumption into particular appliances in domestic or industrial environments. NIALM …

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 …

This paper proposes a new event detection algorithm for the use in Non-Intrusive Load
Monitoring (NILM). This latter is a field where the main concern is to break down, in a non …

The nonintrusive load monitoring (NILM) process aims to monitor the different appliances
connected to a power grid. It accomplishes this by analyzing one or multiple signals …

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of
signal processing techniques to extract features from voltage and current signals. This paper …

In this article an energy disaggregation architecture using elastic matching algorithms is
presented. The architecture uses a database of reference energy consumption signatures …

A data-driven methodology to improve the energy disaggregation accuracy during Non-
Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification …

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 …

An extension of the baseline non-intrusive load monitoring approach for energy
disaggregation using temporal contextual information is presented in this paper. In detail …