19 citations · 46 across the 14 of their papers we have counts for
6 papers · 1 filter
Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
Varsha Pendyala, Yiwei Fu, Weizhong Yan +1
Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advan…
RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
Anmol Dwivedi, Ali Tajer, Santiago Paternain +1
Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both. This paper introduces a phy…
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand
Yiwei Fu, Nurali Virani, Honggang Wang
Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based…
Masked Multi-Step Multivariate Time Series Forecasting with Future Information
Yiwei Fu, Honggang Wang, Nurali Virani
In this paper, we introduce Masked Multi-Step Multivariate Forecasting (MMMF), a novel and general self-supervised learning framework for time series forecasting with known future…
Variational Encoder-based Reliable Classification
Chitresh Bhushan, Zhaoyuan Yang, Nurali Virani +1
Machine learning models provide statistically impressive results which might be individually unreliable. To provide reliability, we propose an Epistemic Classifier (EC) that can pr…
Justification-Based Reliability in Machine Learning
Nurali Virani, Naresh Iyer, Zhaoyuan Yang
With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need…