43 citations · 100 across the 7 of their papers we have counts for
7 papers
Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains
Weijie Xia, Gao Peng, Chenguang Wang +3
Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as…
A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
Weijie Xia, Chenguang Wang, Peter Palensky +1
Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g.,…
Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder
Chenguang Wang, Ensieh Sharifnia, Simon H. Tindemans +1
Variational autoencoder (VAE) neural networks can be trained to generate power system states that capture both marginal distribution and multivariate dependencies of historical dat…
Generating Contextual Load Profiles Using a Conditional Variational Autoencoder
Chenguang Wang, Simon H. Tindemans, Peter Palensky
Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especiall…
Generating Multivariate Load States Using a Conditional Variational Autoencoder
Chenguang Wang, Ensieh Sharifnia, Zhi Gao +2
For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the ava…
Training Strategies for Autoencoder-based Detection of False Data Injection Attacks
Chenguang Wang, Kaikai Pan, Simon Tindemans +1
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can pot…
Detection of False Data Injection Attacks Using the Autoencoder Approach
Chenguang Wang, Simon Tindemans, Kaikai Pan +1
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventi…