Electric power systems provide the backbone of modern industrial societies. Enabling
scalable grid analytics is the keystone to successfully operating large transmission and …

Precise electric load forecasting at different time horizons is an essential aspect for electricity
producers and consumers who participate in energy markets in order to maximize their …

The cyber–physical infrastructure of a smart grid requires data-dependent artificial
intelligence (AI)-based forecasting schemes for predicting different aspects for the short-to …

Grid forming inverters provide voltage and frequency regulations for microgrids; in the
meantime, new challenges are introduced for microgrid protections. For instance, inverters' …

Standard methods for forecasting electricity loads are not robust to cyberattacks on electricity
demand data, potentially leading to severe consequences such as major economic loss or a …

Load forecasting can effectively reduce the operating costs of the power industry, while
attacks on the load can lead to inaccurate forecasts. In the existing reports, the robust …

Hybridization of classifiers can often yield outperformed performance compared to its best
individual component and mostly have more generalization ability. The majority of combined …

Background: Load forecasting is a crucial element in power utility business load forecasting
and has influenced key decision-makers in the industry to predict future energy demand with …

Forecasting energy demand is critical to ensure the steady operation of the power system.
However, present approaches to estimating power load are still unsatisfactory in terms of …

This paper underpins the potential of quantum generative adversarial networks (QGANs) for
renewable scenario generation in power grids. A single QGAN with either amplitude or …