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STREAM: An Objective-Driven and Uncertainty-Aware Framework for Industrial Energy Data Acquisition

Zhipeng Ma, Bo Nørregaard Jørgensen, Zheng Grace Ma

arXiv:2608.26754v1cs.IR

TL;DR

Industrial energy datasets often remain unsuitable for energy-performance assessment despite being accessible through connected infrastructures. The paper develops and evaluates STREAM, a six-stage objective-driven and uncertainty-aware acquisition framework, and finds across two batch-process cases that traceability and evidence expose restrictions on reliable batch-level analysis.

  • Problem

    Industrial acquisition workflows often emphasize connectivity and storage without verifying whether accessible signals satisfy the requirements of a defined energy-performance assessment.

  • Method

    STREAM links objectives to indicators, required variables, sources, metadata, uncertainty records, and repositories through six operational stages.

  • Results

    Across induction-furnace melting and cheese-powder drying, accessible data can remain unsuitable for reliable batch-level analysis when calibration, timing, contextual linkage, or transformation evidence is incomplete.

  • Takeaways & Limitations

    STREAM provides a repeatable procedure for diagnosing data readiness, identifying measurement and integration gaps, and preparing traceable datasets for monitoring, benchmarking, and decision support.

  • Takeaways & Limitations

    The evaluation does not measure implementation effort, inter-rater agreement, indicator changes after alignment or exclusion, or automated metadata-extraction performance.

Abstract

from arXiv · show

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.

1 Introduction

Industrial energy assessment requires process context, yet accessible industrial data often lack the evidence needed for analytical suitability. STREAM addresses this gap by linking energy objectives to requirements, sources, metadata, uncertainty records, and repositories through an operational workflow.

  • Industrial energy performance depends on equipment states, production volumes, material properties, schedules, temperatures, and control decisions, so aggregate energy measurements alone are insufficient.
  • Accessible signals may lack required variables, timing, context, calibration evidence, or transformation history because factory infrastructures typically serve control, monitoring, maintenance, or safety.
  • The paper addresses the lack of an operational framework for verifying whether industrial sensors and production data fit a defined energy objective before indicators or decisions are produced.
  • STREAM structures acquisition as a traceable chain from objective definition through variable specification, source verification, uncertainty documentation, and database migration.
  • The paper operationalizes the conceptual six-stage STREAM sequence with artifacts, evidence gates, suitability classes, metadata requirements, an uncertainty rubric, and case-specific matrices.
  • Two industrial batch-process cases test whether STREAM can diagnose data readiness and prepare traceable datasets for monitoring, benchmarking, and decision support rather than claim energy savings.

2 Background and Related Work

Industrial data infrastructures provide broad access but do not guarantee that signals are suitable for a specified energy-performance analysis. The background motivates objective-driven requirements and explicit treatment of measurement, temporal, contextual, and processing uncertainty.

  • Industrial energy and process data are distributed across heterogeneous sensors, control systems, SCADA, IoT platforms, and production databases, creating integration issues involving timestamps, formats, provenance, and governance.
  • Connectivity and storage do not ensure analytical suitability because signals may have inadequate sampling, missing calibration evidence, unclear asset relationships, or insufficient process context.
  • Metadata supports interpretation and reuse through identifiers, units, sampling intervals, measurement characteristics, calibration status, installation context, and transformation history, but industrial metadata are often fragmented.
  • Energy data become analytically meaningful only when connected to defined indicators and decision uses, each imposing requirements on boundaries, resolution, normalization, process state, and acceptable uncertainty.
  • STREAM separates accessibility from analytical suitability and classifies sources as suitable, conditional, unsuitable, or unavailable for a specified indicator.
  • Measurement, temporal, contextual, and processing uncertainty concern respectively sensor evidence, timing, links to process entities, and transformations such as interpolation or aggregation.
  • Monitoring may tolerate moderate timing or contextual uncertainty, whereas batch benchmarking requires reliable synchronization of energy, production, and process data.
  • Data quality describes observable dataset properties, while uncertainty concerns incomplete knowledge; therefore, complete datasets may still lack sufficient evidence for intended use.

3 The Objective-Driven and Uncertainty-Aware STREAM Framework

STREAM is a six-stage, objective-driven workflow that carries requirements, source evidence, metadata, and uncertainty records from an energy question to an analytical repository. Its conservative suitability logic supports documented use, restriction, or rejection of data.

  • Overview: STREAM comprises Specification of Objectives, Technical Requirements, Resource Mapping, Extraction from Sensors, Archival Metadata, and Migration to Database.
  • Overview: Uncertainty is recorded across all six stages so evidence problems can be addressed before or during extraction, metadata archival, and database migration.
  • Overview: A source is not analysis-ready until required evidence is verified or its remaining limitations are documented.
  • Operational protocol: The workflow proceeds iteratively: failed evidence gates trigger defined failure actions and return the process to the earliest affected stage.
  • Specification and requirements: Specification of Objectives defines the decision, process boundary, intended indicator, and analytical use, while Technical Requirements translates the indicator into variables, tolerances, and measurement specifications.
  • Resource mapping: Resource Mapping documents candidate sources and produces source-to-requirement maps, measurement-gap lists, and suitability classifications for required variables.
  • Resource mapping: Suitability is classified as suitable, conditional, unsuitable, or unavailable according to critical requirements and documented mitigation possibilities.
  • Extraction: Extraction preserves source, timing, quality, and processing information, while corrected, resampled, interpolated, aggregated, or estimated values receive explicit processing status.

4 Case Studies

The evaluation applies STREAM to two industrial batch processes in distinct data environments: induction-furnace melting using IoT data and cheese-powder drying integrating SCADA with production orders. Both cases test contextual and temporal readiness for energy analysis rather than quantify savings.

  • Cross-case design: The two cases use the same operational protocol and suitability rule while testing STREAM across different data environments.
  • Case Study 1: Foundry: The foundry case examines induction-furnace melting and relates electricity consumption to production and process conditions before benchmarking.
  • Case Study 1: Foundry: Case Study 1 assesses electricity, temperature, material quantity, equipment state, and batch records against requirements including calibration, timestamp alignment, and furnace-to-batch linkage.
  • Case Study 1: Foundry: STREAM maps the foundry’s required variables to IoT meters, sensors, control-state tags, and production records while preserving timestamped observations and raw state changes.
  • Case Study 2: Cheese powder: The cheese-powder case focuses on drying-tower energy use and relates electricity and heating consumption to production conditions.
  • Case Study 2: Cheese powder: Case Study 2 integrates SCADA energy and process tags, heating records, sensors, quantities, and order data, with readiness requirements for synchronization, recipe linkage, and transformation history.
  • Case Study 2: Cheese powder: The second case extracts SCADA and order data separately while retaining source timestamps and clock information.

5 Results

Across both industrial cases, STREAM produced traceable data-readiness assessments while showing that extraction feasibility does not guarantee unrestricted analytical suitability. The framework identified uncertainty and evidence gaps that determine whether datasets support monitoring, benchmarking, or restricted use.

  • Case Study 1: In the foundry case, all five variable groups were mapped, but none was fully suitable for unrestricted batch-level benchmarking.Electricity, furnace temperature, batch timing, and equipment status were conditionally suitable; material quantity had restricted analytical use.
  • Case Study 1: The foundry dataset supported trend monitoring and exploratory analysis, while batch benchmarking remained conditional because calibration, timestamp alignment, material evidence, batch linkage, and state definitions were incomplete.Gaps occurred across measurement, temporal, contextual, and processing uncertainty categories, and corrective action was specified for each variable group.
  • Case Study 2: In the drying case, all six variable groups were mapped, but none was fully suitable for unrestricted order- or recipe-level benchmarking.Heating demand had restricted analytical use, while the other groups were conditionally suitable.
  • Case Study 2: The drying assessment found extraction and migration feasible, but heating-data quality, clock synchronization, linkage, quantity evidence, and transformation provenance remained limiting.Incomplete calibration evidence, low-quality heating data, sampling-rate differences, and partial order, recipe, or quantity links reduced readiness for batch- or recipe-level benchmarking.
  • Cross-case evaluation: All six STREAM stages produced traceable links from energy objectives to analytical repositories in both IoT- and SCADA-based environments.The cross-case evaluation characterizes STREAM as a diagnostic framework that makes evidence requirements for trustworthy indicators visible.
  • Cross-case evaluation: Across cases, accessible energy and temperature traces supported monitoring trends, whereas benchmarking required reliable material quantities, batch boundaries, equipment states, time bases, and contextual links.STREAM classified variables as suitable, conditional, unsuitable, or unavailable and required uncertain batches to be restricted or excluded rather than assigned energy through undocumented assumptions.

6 Discussion

The discussion positions STREAM as an operational acquisition framework that distinguishes connectivity from analytical suitability and supports traceable, uncertainty-aware decisions across industrial data environments.

  • Contributions: The same six-stage procedure was applied to IoT-based furnace data and to integrated SCADA and production-order data.The cases tested different data environments rather than quantified energy savings.
  • Contributions: Incomplete calibration, heterogeneous sampling, weak synchronization, partial asset mapping, missing context, and undocumented transformations reduced dataset confidence in both cases.These findings show that accessible signals may remain unsuitable for a defined assessment.
  • Comparison with Existing Approaches: STREAM combines objective definition, evidence gates, source-suitability decisions, metadata, uncertainty assessment, and corrective actions in one operational sequence.Its outputs include a source map, evidence record, suitability classification, and corrective-action list.
  • Implementation Implications: STREAM should precede indicator calculation because its stages classify existing records as suitable, conditionally usable, or requiring complementary evidence.The workflow preserves unknown values, quality flags, provenance, and processing status.
  • Implementation Implications: Infrastructure improvement may require documentation, database or SCADA reconfiguration, sensor recalibration, or new measurements.The framework supports prioritizing these interventions according to identified evidence gaps.
  • Limitations and Future Work: The evaluation did not measure implementation effort, inter-rater agreement, indicator changes after alignment or exclusion, or automated metadata-extraction performance.Cross-case implications remained mainly qualitative because comparable numerical records were unavailable.
  • Limitations and Future Work: Future work should quantify readiness and extend evaluation to additional sectors and continuous production processes.Proposed measures include metadata completeness, timestamp-alignment error, contextual-linkage coverage, and records affected by undocumented transformations.

7 Conclusion

STREAM is presented as an objective-driven, uncertainty-aware framework linking energy-assessment objectives to traceable data acquisition. Evaluations in foundry melting and cheese-powder drying show that accessible data may remain unsuitable for reliable batch analysis when key evidence is incomplete.

  • Framework: STREAM links objectives to indicators, required variables, physical and digital sources, metadata, uncertainty records, and analytical repositories through six stages.The framework determines whether industrial sensor and production data suit a specified energy-performance assessment.
  • Evaluation: The framework was evaluated in induction-furnace melting and cheese-powder drying, supporting source mapping, metadata documentation, uncertainty assessment, and data-warehouse migration.Both cases involved industrial batch processes.
  • Results: Accessible data can remain unsuitable for reliable batch-level analysis when calibration evidence, timestamp alignment, batch linkage, process context, or transformation history is incomplete.This finding directly separates data accessibility from analytical suitability.
  • Contributions: The practical contribution is a repeatable procedure for diagnosing data readiness, identifying measurement and integration gaps, and preparing traceable datasets for monitoring, benchmarking, and decision support.The scientific contribution integrates objective-driven requirements, source-suitability assessment, metadata archival, repository migration, and uncertainty documentation.
  • Limitations: The study is limited by quantitative uncertainty assessment, reliance on domain expertise, and evaluation in two anonymized batch-process cases.Future work includes quantifying uncertainty propagation and testing additional sectors and continuous processes.

Appendix

The appendix supplies templates and case-specific matrices for documenting metadata, data-quality risks, variable-source traceability, suitability, and uncertainty. It also records evidence and corrective actions for temporal, contextual, processing, and measurement issues.

  • Appendix Artifacts: Appendix Tables A1–A6 provide metadata templates, uncertainty rubrics, and case-specific traceability matrices supporting the main text.The materials are designed for traceable and uncertainty-aware energy-data acquisition.
  • Uncertainty Assessment: The uncertainty assessment organizes evidence into temporal, contextual, processing, and measurement categories with low, moderate, and high-or-unknown risk levels.The appendix identifies evidence such as sampling intervals, asset and order links, filtering or resampling, and calibration history.
  • Case Study 1: Case 1 uses IoT timestamps, control-state events, start and end definitions, clock source, and furnace ID to assess temporal and contextual suitability.Recommended actions include timestamp synchronization, batch-boundary documentation, state validation, and preservation of raw state-change records.
  • Case Study 2: Case 2 uses production or order records, SCADA timestamps, recipes, product quantities, state tags, and processing scripts to assess linkage and transformation risks.Recommended actions include synchronizing records, strengthening quantity–recipe–order linkage, validating state logic, and documenting transformations.
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