2 citations · 7 across the 15 of their papers we have counts for
6 papers · 1 filter
Mycelium: A Generalizable Cross-Grid Multi-Task Model for Electrical Distribution Systems
Zhengyang Wei, Shourya Bose, Helgi Hilmarsson +2
Electrical distribution grid operations require inference across heterogeneous networks from sparse, noisy, and incomplete time series measurements. In this work, we identify chall…
Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
Shourya Bose, Helgi Hilmarsson, Dhruv Suri
Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on h…
WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
Dhruv Suri, Helgi Hilmarsson, Shourya Bose
Solving AC Optimal Power Flow (AC-OPF) is of central importance in electricity market operations, where interior-point methods (IPMs) such as IPOPT are the standard solvers. A grow…
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…
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients
Shourya Bose, Kibaek Kim
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting (STLF) models. In response to privacy concerns, fede…