{
  "abstract": "Accurate modeling and short-term forecasting of electric arc furnace (EAF) voltage are important for power quality analysis, process monitoring, and the development of data-driven compensation and control strategies. The strongly nonlinear and time-varying behavior of the electric arc makes its voltage difficult to represent using simplified analytical or low-order parametric models. This study compares several modeling approaches using industrial voltage and current measurements acquired on the secondary side of a three-phase furnace transformer. The investigated methods include a hybrid Fourier–polynomial model optimized by a genetic algorithm, a CNN–GRU model, LSTM and BiLSTM networks, and a Dilated CNN–BiLSTM architecture. The models are first evaluated for same-interval voltage estimation over a common measurement interval using RMSE, MAE, R2, and a current-interval V–I RMSE. A separate direct multi-horizon forecasting experiment is then performed using a 40 ms historical voltage–current window and prediction horizons of 1, 2, 5, 10, 20, 40, and 60 ms. Persistence and one-period predictors are used as reference baselines. For same-interval estimation, the Dilated CNN–BiLSTM achieves the lowest mean errors across three independent runs, with an RMSE of 5.33 ± 0.59 V, an MAE of 4.13 ± 0.44 V, an R2 of 0.99986 ± 0.00003, and a current-interval V–I RMSE of 1.70 ± 0.41 V. The BiLSTM obtains the next-lowest mean RMSE, 9.26 ± 4.25 V. In the forecasting task, no single neural architecture achieves the lowest RMSE at all prediction horizons. The BiLSTM provides the lowest neural model RMSE at 5 and 20 ms, the LSTM at 2, 10, and 60 ms, while the BiLSTM and Dilated CNN–BiLSTM yield nearly identical RMSE values at 1 ms. At 40 ms, the one-period baseline produces a lower RMSE than the neural models. The results show that forecasting performance is strongly horizon-dependent and that increased architectural complexity does not necessarily guarantee lower forecasting error. This study also demonstrates the importance of distinguishing same-interval voltage estimation from true future forecasting when evaluating EAF voltage models.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical Engineering and Industrial Informatics, University Politechnica Timisoara, 300006 Timisoara, Romania"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Manuela Panoiu",
      "orcid": "0000-0001-8499-1080"
    },
    {
      "affs": [
        0
      ],
      "name": "Caius Panoiu",
      "orcid": "0000-0002-4965-2339"
    },
    {
      "affs": [
        0
      ],
      "name": "Loredana Ghiormez",
      "orcid": "0000-0001-5955-7530"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/71z9rda9xz2w1.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203710",
  "fulltext_url": null,
  "id": "71z9rda9xz2w1",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3710",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Power Quality and Harmonics"
    ]
  ],
  "openalex": "W7221602859",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/71z9rda9xz2w1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203710",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3710"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Comparative Time-Series Modeling and Short-Term Forecasting of Electric Arc Furnace Voltage Using Hybrid and Deep Learning Approaches",
  "title_text": "Comparative Time-Series Modeling and Short-Term Forecasting of Electric Arc Furnace Voltage Using Hybrid and Deep Learning Approaches",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/71z9rda9xz2w1",
  "venue": "Mathematics",
  "versions": []
}