The machine fault diagnosis is being considered in a larger-scale complex system with
numerous measurements from diverse subsystems or components, where the collected data …

With increasing levels of renewable energy powering our electricity system, data that tracks
energy generation, distribution and consumption has never been more important. For …

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load
components has thus far mostly been studied using univariate data, eg, using only whole …

Complex industrial process data often exhibit nonlinear static and dynamic characteristics.
Traditional deep learning methods such as stacked autoencoder (SAE) have excellent …

Anomaly detection of steam turbines is to recognize infrequent instances within sensor data
that plays a vital role in stable power supply. Machine learning models have been applied to …

Nowadays, rooftop photovoltaic (PV) panels are getting enormous attention as clean and
sustainable sources of energy due to the increasing energy demand, depreciating physical …

In recent years, sustainable sources of energies attract significant interest due to the serious
environmental issues of fossil fuels. Rooftop photovoltaic (PV) panels are among the …

Performance monitoring, anomaly detection, and root-cause analysis in complex cyber–
physical systems (CPSs) are often highly intractable due to widely diverse operational …

To discover the spatial and temporal traffic patterns, this paper proposes a spatiotemporal
graphical modeling approach, spatiotemporal pattern network (STPN), to explore traffic …

In this paper optimal grid design problems are revisited in view of the ongoing
transformations in distribution systems. The transformations are those caused by distributed …