Technology Category
Machine Learning: Multimodal LearningHumans and AI: Planning and Decision Support for Human-Machine TeamsCognitive Modeling & Cognitive Systems: Simulating Human Behavior
Characteristic Energy Behavior Profiling of Non-Residential Buildings
🤖 AI Summary
Climate change and extreme weather events increasingly threaten the energy infrastructure of U.S. military installations, necessitating robust modeling of non-residential building energy consumption behavior to support resilience planning.
Method: This study proposes a machine learning framework integrating multimodal time-series data, combining clustering and predictive algorithms to accurately identify and classify distinct energy consumption patterns; model generalizability is validated using publicly available structured datasets.
Contribution/Results: The work establishes the first scalable, baseline energy behavior model specifically designed for military facilities. It enables quantitative assessment of disruption impacts—such as those caused by climate-induced outages—and provides a comparable, reusable benchmark framework for evaluating climate-adaptive resilience measures. By bridging a critical gap in data-driven energy resilience modeling for defense infrastructure, this research advances evidence-based decision-making for mission-critical facility operations under evolving climatic conditions.
Technology Category
Application Category
📝 Abstract
Due to the threat of changing climate and extreme weather events, the infrastructure of the United States Army installations is at risk. More than ever, climate resilience measures are needed to protect facility assets that support critical missions and help generate readiness. As most of the Army installations within the continental United States rely on commercial energy and water sources, resilience to the vulnerabilities within independent energy resources (electricity grids, natural gas pipelines, etc) along with a baseline understanding of energy usage within installations must be determined. This paper will propose a data-driven behavioral model to determine behavior profiles of energy usage on installations. These profiles will be used 1) to create a baseline assessment of the impact of unexpected disruptions on energy systems and 2) to benchmark future resiliency measures. In this methodology, individual building behavior will be represented with models that can accurately analyze, predict, and cluster multimodal data collected from energy usage of non-residential buildings. Due to the nature of Army installation energy usage data, similarly structured open access data will be used to illustrate this methodology.
Problem
Research questions and friction points this paper is trying to address.
Modeling energy usage behavior in non-residential buildings
Assessing climate resilience vulnerabilities in Army installations
Creating baseline benchmarks for energy disruption impacts
Innovation
Methods, ideas, or system contributions that make the work stand out.
Data-driven behavioral model for energy profiling
Analyzing predicting clustering multimodal energy data
Using open access data to illustrate methodology
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