Forecasting or Contemporaneous Estimation? A Leakage-Aware Industrial Energy Audit with Uncertainty Quantification

Esam Miftah Abdulnabi; Nabeel Faraj Amhimmid; Ashraf Faraj Saed Albarki; Faraj Ahmed Mohammed Alfarisi

doi:10.64943/ljacs.2026.010204

Authors

Esam Miftah Abdulnabi Department of Computer, College of Science and Technology Qumins, Qumins, Libya https://orcid.org/0009-0000-3544-2827
Nabeel Faraj Amhimmid Department of Computer, College of Science and Technology Qumins, Qumins, Libya https://orcid.org/0009-0008-6311-4036
Ashraf Faraj Saed Albarki Department of Computer Science, College of Arts and Sciences Qumins, University of Benghazi, Qumins, Libya https://orcid.org/0009-0009-5934-6317
Faraj Ahmed Mohammed Alfarisi Department of Computer, College of Science and Technology Qumins, Qumins, Libya https://orcid.org/0009-0006-7719-9363

DOI:

https://doi.org/10.64943/ljacs.2026.010204

Keywords:

industrial energy forecasting;, data leakage, temporal, conformal prediction, uncertainty, random forest, XGBoost, smart manufacturing

Abstract

Short-term industrial energy forecasting supports load planning only when every predictor is available at forecast issuance. This study audits 15- and 60-minute forecasting with 35,040 real observations from a South Korean steel facility. We reconstruct a continuous 15-minute timeline, define a deployable 39-feature protocol using measurements available no later than the forecast origin, and contrast it with two diagnostic protocols that admit target-time sensors. Ridge regression, Random Forest, and XGBoost are compared with persistence and daily and weekly seasonal-naive baselines under chronological and random 70/15/15 train/calibration/test partitions. The clean chronological Random Forest achieved mean absolute error (MAE) of 3.829 kWh at 15 minutes and 7.484 kWh at 60 minutes, reducing MAE relative to persistence by 23.35% and 35.31%. Paired moving-block bootstrap intervals excluded zero for every comparison with the operational baselines. By contrast, target-time measurements reduced XGBoost MAE by 82.72% and 90.12%, showing that sub-1-kWh results describe contemporaneous estimation rather than deployable forecasting. Target-time CO2 was a strong proxy: its correlation with energy use was 0.988, and 97.69% of values equaled rounded usage multiplied by 0.00045. Split-conformal intervals achieved near-nominal marginal coverage, yet 90% peak-load coverage fell to 51.99% and 44.70%. The audit shows that feature-availability semantics and conditional uncertainty assessment affect interpretation more than the choice between strong tree learners.

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