Quantum computing in power systems
Electric power systems provide the backbone of modern industrial societies. Enabling
scalable grid analytics is the keystone to successfully operating large transmission and …
scalable grid analytics is the keystone to successfully operating large transmission and …
[HTML][HTML] Electric load forecasting under False Data Injection Attacks using deep learning
A Moradzadeh, M Mohammadpourfard, C Konstantinou… - Energy Reports, 2022 - Elsevier
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 …
producers and consumers who participate in energy markets in order to maximize their …
Resiliency of forecasting methods in different application areas of smart grids: A review and future prospects
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 …
intelligence (AI)-based forecasting schemes for predicting different aspects for the short-to …
AI-enabled traveling wave protection for microgrids
Grid forming inverters provide voltage and frequency regulations for microgrids; in the
meantime, new challenges are introduced for microgrid protections. For instance, inverters' …
meantime, new challenges are introduced for microgrid protections. For instance, inverters' …
Robust regression for electricity demand forecasting against cyberattacks
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 …
demand data, potentially leading to severe consequences such as major economic loss or a …
An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting
S Zhao, Q Wu, Y Zhang, J Wu, XA Li - Expert Systems with Applications, 2022 - Elsevier
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 …
attacks on the load can lead to inaccurate forecasts. In the existing reports, the robust …
A linear directional optimum weighting (LDOW) approach for parallel hybridization of classifiers
Hybridization of classifiers can often yield outperformed performance compared to its best
individual component and mostly have more generalization ability. The majority of combined …
individual component and mostly have more generalization ability. The majority of combined …
A Comprehensive Review of Various Machine Learning Techniques used in Load Forecasting
DP Mohan, MSP Subathra - Recent Advances in Electrical & …, 2023 - ingentaconnect.com
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 …
and has influenced key decision-makers in the industry to predict future energy demand with …
[HTML][HTML] ELFNet: An Effective Electricity Load Forecasting Model Based on a Deep Convolutional Neural Network with a Double-Attention Mechanism
P Zhao, G Ling, X Song - Applied Sciences, 2024 - mdpi.com
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 …
However, present approaches to estimating power load are still unsatisfactory in terms of …
Quantum renewable scenario generation
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 …
renewable scenario generation in power grids. A single QGAN with either amplitude or …