@article{SchmittMillerSchelleretal.2026,
  author    = {Schmitt, Anna-Maria and Miller, Eddi and Scheller, Fabian and Schmitt, Jan},
  title     = {Time-Series Modelling for Energy Consumption Prediction in CNC Milling with Regenerative Drives},
  journal   = {Procedia CIRP},
  volume    = {140},
  publisher = {Elsevier BV},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2026.05.148},
  institution = {Fakult{\"a}t Wirtschaftsingenieurwesen; Institut Digital Engineering (IDEE); Institut f{\"u}r Sustainable Energy Systems (INSYS)},
  pages     = {881 -- 885},
  year      = {2026},
  abstract  = {Accurately predicting the energy demand of Computerized Numeric Control (CNC) machining processes before production enables the assessment of a product's CO₂ footprint, the identification of optimization opportunities, and the implementation of energy-aware scheduling strategies. However, forecasting the energy consumption of CNC machines equipped with regenerative drives presents unique challenges, as the energy demand of a given G-command is influenced by the preceding operation. This study investigates the application of time-series Machine Learning (ML) models to better capture these temporal dependencies and improve energy consumption accuracy. A significant variance in repeated measurements was observed during the experimental phase, prompting a comparative analysis of using raw versus averaged energy values as input data. Multiple time-series model architectures, including Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCNs), are evaluated for their ability to learn sequential patterns in a 5-axis machining process. The results reveal that while ensemble methods such as LightGBM and Random Forest achieve the highest accuracy and efficiency on the test dataset, sequence-based models demonstrate greater robustness on unseen validation data. Incorporating a small portion of validation data into training further improves ensemble performance, highlighting the trade-off between robustness and efficiency in energy demand prediction.},
  language  = {en}
}