Publications

Oct, 2024

ORNL Report

Neighborhood of the Future

Jun, 2024

Journal

Quantifying the Power System Resilience of the US Power Grid Through Weather and Power Outage Data Mapping

Recent increases in extreme weather events such as severe thunderstorms, floods, and hurricanes are leading to destruction in power system equipment (transmission and distribution poles and lines, substations, power plants, etc.) and are causing widespread prolonged power outages. These outages often cause inconveniences in critical services (health care, transportation, n…

Jun, 2024

Journal

Visual Brick model authoring tool for building metadata standardization

The Brick ontology is a unified semantic metadata standard for building assets and their relationships, serving as a key enabler for effective interoperability and automation of building systems and analytics. However, creating a Brick model, in other words, standard semantic metadata based on the Brick ontology for a building dataset, can be a complex task. This paper pre…

May, 2024

Journal

Active learning of neural network potentials for rare events

Atomistic simulation with machine learning-based potentials (MLPs) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while bei…

Mar, 2024

Journal

A dataset of recorded electricity outages by United States county 2014–2022

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualizati…

Dec, 2023

Journal

Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with pos…

Dec, 2023

Conference Paper

Toward Quantifying Vulnerabilities in Critical Infrastructure Systems

Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. Vulnerability in critical infrastructures (CIs) is defined as a measure of system susceptibility to threat scenarios. Quantifying vulnerability in CIs has not been adequately addressed in the literature. This paper presents ongoing research on how the aut…

Dec, 2023

Conference Paper

Advanced Health Information Technology Analytic Framework and Application to Hazard Detection

Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in ad…

Dec, 2023

Journal

COVID-19 Pandemic Ramifications on Residential Smart Homes Energy Use Load Profiles

The COVID-19 pandemic has significantly affected people’s behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we…

Nov, 2023

ORNL Report

MODELS AND METHODS FOR ASSESSING THE VALUE OF HVDC AND MVDC TECHNOLOGIES IN MODERN POWER GRIDS

Nov, 2023

Conference Paper

ScaleML: Machine Learning based Heap Memory Object Scaling Prediction

Memory subsystem contributes 28-40% of total energy consumption. Several studies investigated energy prediction and consumption via profiling memory object access patterns. However, such profiling leads to higher energy consumption due to intense memory object-level profiling to achieve high prediction accuracy. Further, memory object access pattern prediction has been con…

Nov, 2023

Journal

Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML…