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Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants — arXiv:2611.01425

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01425
License (as archived): CC0-1.0
Archived: 2026-11-03T18:36:53.354Z
SHA-256 of the archived text: e1456739bfdbd369eaab7ca5358ff6ffa8c1009cd8a503318afff672db325a0e
Stable URL of this page: https://metraipolis.com/documento/dc_4f32b770cdc8

How to cite

Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants — arXiv:2611.01425 — arXiv (en), https://arxiv.org/abs/2611.01425 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4f32b770cdc8 (Metraipolis archive dc_4f32b770cdc8, sha256 e1456739bfdbd369eaab7ca5358ff6ffa8c1009cd8a503318afff672db325a0e)

BibTeX

@misc{dc4f32b770cdc8,
  title = {Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants — arXiv:2611.01425},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01425},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4f32b770cdc8; sha256 e1456739bfdbd369eaab7ca5358ff6ffa8c1009cd8a503318afff672db325a0e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01425. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper presents a multimodal generative model designed to simulate a solar energy plant system for predictive maintenance and fault prediction. We propose a novel approach to enhance system generation through conditional generation by leveraging both event sequences and time-series data and improve next event prediction. The framework employs a Transformer Hawkes Process (THP) to encode event data and an iTransformer to encode time-series data, integrated via a co-attention mechanism that effectively captures cross-modal dependencies between discrete events and continuous signals. From these integrated representations, we employ two specialized decoders for simulation: a Conditional Latent Diffusion Model (CLDM) for modeling future sensor trajectories and the Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process (SMURF-THP) for high-fidelity event prediction. This dual-decoder strategy supports uncertainty-aware, history-conditioned simulation by rolling the model forward for a specified number of generation iterations, producing finite stochastic scenarios rather than deterministic extrapolations over the full observational record. Evaluated on a comprehensive dataset from the Red Rock Solar Site in Arizona, our approach improves aggregate event-prediction F1 relative to baselines and supports applications in predictive maintenance, fault detection, and energy optimization for solar power systems.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2611.01425 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:12:54.499Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).