Problem
Research questions and friction points this paper is trying to address.
Industrial Energy Management
Data Acquisition
Energy-Performance Assessment
Uncertainty-Aware
STREAM: An Objective-Driven and Uncertainty-Aware Framework for Industrial Energy Data Acquisition
🤖 AI Summary
为解决工业能源数据采集问题，提出STREAM框架，通过目标驱动和不确定性评估方法确保数据满足能源性能评估需求。
📝 Abstract
Industrial energy management requires datasets that connect energy use with equipment states, production batches, material flows, and process conditions. However, conventional acquisition workflows commonly emphasize connectivity and storage without verifying whether accessible signals satisfy the requirements of a defined energy-performance assessment. This paper presents STREAM, an objective-driven and uncertainty-aware framework comprising Specification of Objectives, Technical Requirements, Resource Mapping, Extraction from Sources, Archival Metadata, and Migration to Database. STREAM is the central workflow: objective-to-data traceability is its end-to-end output, while measurement, temporal, contextual, and processing uncertainty are assessed across all six stages. Compared with the original conceptual STREAM sequence, this paper adds stage-level artifacts, minimum-evidence gates, source-suitability rules, a metadata template, an uncertainty rubric, and case-specific traceability matrices. The framework is validated through two industrial batch-process cases: induction-furnace melting in a foundry and cheese-powder drying using SCADA and production-order data. The results demonstrate that data accessibility is not equivalent to analytical suitability and show how STREAM supports transparent decisions about immediate data use, analytical restrictions, and prioritized infrastructure improvements.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Objective-Driven
Uncertainty-Aware
Data Acquisition
Industrial Energy Management
Traceability
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