Non-intrusive load monitoring (NILM) is the task of disaggregating the total power
consumption into its individual sub-components. Over the years, signal processing and …

Unveiling feeder topologies from data is of paramount importance to advance situational
awareness and proper utilization of smart resources in power distribution grids. This tutorial …

This paper provides a survey of big data analytics applications and associated
implementation issues. The emphasis is placed on applications that are novel and have …

This paper presents a detailed analysis of big data and machine learning (ML) in the
electrical power and energy sector. Big data analytics for smart energy operations …

In recent years, data privacy has attracted increasing public concerns, especially with public
scandals from top Internet companies, emerging over inappropriate data collection, lack of …

This paper presents a novel nonlinear dictionary learning (DL) model to address the energy
disaggregation (ED) problem, ie, decomposing the electricity signal of a home to its …

This paper presents an overview of the evolution of knowledge extraction from power
systems data since 1980's up to date. As the existing literature in this application domain is …

Smart Cities (SCs) have recently opened new lifestyles as they introduce effective
approaches for improving urban management. These cities integrate Industry 4.0 …

Big data analytics is a virtually new term in power system terminology. This concept delves
into the way a massive volume of data is acquired, processed, analyzed to extract insight …

This paper proposes a Multi-task Logistic Low-Ranked Dirty Model (MT-LLRDM) for fault
detection in power distribution networks by using the distribution Phasor Measurement Unit …