2 citations · 2 across the 3 of their papers we have counts for
5 papers
Physics-Informed Gradient Estimation for Accelerating Deep Learning based AC-OPF
Kejun Chen, Shourya Bose, Yu Zhang
The optimal power flow (OPF) problem can be rapidly and reliably solved by employing responsive online solvers based on neural networks. The dynamic nature of renewable energy gene…
From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
Shourya Bose, Yijiang Li, Amy Van Sant +2
Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting usi…
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
Shourya Bose, Yu Zhang, Kibaek Kim
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting models. In response to privacy concerns, federated l…
Presolving Convexified Optimal Power Flow with Mixtures of Gradient Experts
Shourya Bose, Kejun Chen, Yu Zhang
Convex relaxations and approximations of the optimal power flow (OPF) problem have gained significant research and industrial interest for planning and operations in electric power…
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation
Shourya Bose, Yu Zhang, Kibaek Kim
The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate p…