Schneider A, Weilacher A, Sindel T, Franke J, Fürst J (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: Elsevier B.V.
Book Volume: 146
Pages Range: 744-749
Conference Proceedings Title: Procedia CIRP
Event location: Austin, TX, USA
DOI: 10.1016/j.procir.2026.03.282
Accurate short-term forecasting of active (P), reactive (Q), and apparent power (S) at the production-cell level remains largely unexplored, despite increasing electrification and the prevalence of non-linear, inverter-driven loads in modern manufacturing. These systems exhibit frequency transients and state-dependent dynamics that are not observable in aggregated factory-level data. This work proposes a high-resolution forecasting framework that combines dynamic Working/Standby classification with a benchmarking of Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), Transformers, and Light Gradient-Boosting Machine (LGBM). The results reveal a horizon-dependent performance crossover: TCNs achieve state-of-the-art accuracy for short-term horizons (5 min, R² = 0.88) by capturing deterministic kinematic patterns, while LGBM demonstrates superior robustness at medium horizons (15 min, R² = 0.68) due to its resilience against stochastic production effects. By forecasting not only P but also Q and S, the framework enables predictive power-quality management, including proactive voltage stabilization and dynamic power-factor correction, supporting more reliable and energy-efficient Industry 4.0 microgrids.
APA:
Schneider, A., Weilacher, A., Sindel, T., Franke, J., & Fürst, J. (2026). Multi-Component Power Forecasting in Industrial Production Cells Using Machine Learning. In Dragan Djurdjanovic, Chih-Hao Chang, Michael Cullinan, Wei Li, Zhenhui Shu, Maryam Tilton (Eds.), Procedia CIRP (pp. 744-749). Austin, TX, USA: Elsevier B.V..
MLA:
Schneider, Alexander, et al. "Multi-Component Power Forecasting in Industrial Production Cells Using Machine Learning." Proceedings of the 59th CIRP Conference on Manufacturing Systems, CIRP CMS 2026, Austin, TX, USA Ed. Dragan Djurdjanovic, Chih-Hao Chang, Michael Cullinan, Wei Li, Zhenhui Shu, Maryam Tilton, Elsevier B.V., 2026. 744-749.
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