Training Strategies for Autoencoder-based Detection of False Data Injection Attacks. - Chenguang Wang 006; Kaikai Pan; Simon H. Tindemans; Peter Palensky (ISGT-Europe, 2020)

Detection of False Data Injection Attacks Using the Autoencoder Approach. - Chenguang Wang 006; Simon H. Tindemans; Kaikai Pan; Peter Palensky (CoRR, 2020)

Generating Multivariate Load States Using a Conditional Variational Autoencoder. - Chenguang Wang 006; Ensieh Sharifnia; Zhi Gao; Simon H. Tindemans; Peter Palensky (CoRR, 2021)

Generating Contextual Load Profiles Using a Conditional Variational Autoencoder. - Chenguang Wang 006; Simon H. Tindemans; Peter Palensky (ISGT-Europe, 2022)

Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder. - Chenguang Wang 006; Ensieh Sharifnia; Simon H. Tindemans; Peter Palensky (CoRR, 2023)

Anomaly Detection and Synthetic Data Generation for Power Systems Using Autoencoder Neural Networks. - Chenguang Wang 006 (2023)

A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction. - Weijie Xia; Chenguang Wang 006; Peter Palensky; Pedro P. Vergara (CoRR, 2024)

An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of Domains. - Weijie Xia; Gao Peng; Chenguang Wang 006; Peter Palensky; Eric J. Pauwels; Pedro P. Vergara (CoRR, 2024)

Improved Anomaly Detection and Localization Using Whitening-Enhanced Autoencoders. - Chenguang Wang 006; Simon H. Tindemans; Peter Palensky (IEEE Trans. Ind. Informatics, 2024)