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This paper introduces STREAM, a comprehensive framework designed to enhance industrial energy data acquisition by ensuring that the collected data aligns with specific energy-performance objectives and accounts for various uncertainties. By integrating objective-driven specifications and uncertainty assessments across six stages of data handling, STREAM provides a robust methodology for evaluating data suitability beyond mere accessibility. Validation through two industrial case studies reveals that the framework significantly improves decision-making regarding data use and infrastructure investments, highlighting the critical distinction between data availability and analytical applicability.
Data accessibility is misleading; STREAM reveals how to ensure that what you collect is actually useful for energy performance assessments.
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.
