基于数据建模分析军用设施能耗行为，提升应对断电等风险的韧性。
Characteristic Energy Behavior Profiling of Non-Residential Buildings
- 构建非住宅建筑能耗行为模型，融合多源数据进行分析与预测。
- 通过聚类识别典型能耗模式，为断电等突发事件提供基准评估。
- 适用于军事基地能源韧性规划，也适合其他关键基础设施参考。
由于气候变化和极端天气事件的威胁，美国陆军基地基础设施面临风险。为保护支持关键任务的设施资产并保障战备状态，亟需加强气候韧性措施。由于大部分本土陆军基地依赖商业能源与供水，必须评估其对独立能源资源（如电网、天然气管道）脆弱性的应对能力，并建立能源使用的基础认知。本文提出一种数据驱动的行为建模方法，用于识别基地内非住宅建筑的能耗行为特征。该模型可实现：1）评估突发中断对能源系统的影响基准；2）为未来韧性措施提供对比标准。方法采用能准确分析、预测及聚类多模态能耗数据的个体建筑模型。鉴于陆军基地能源数据的特点，研究采用结构相似的公开数据集来展示该方法的有效性。
原文摘要 · Abstract (English)
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.
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