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Daily peak electrical load forecasting with a multi-resolution approach

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  • معلومة اضافية
    • Contributors:
      Laboratoire de Mathématiques d'Orsay (LMO); Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS); Statistique mathématique et apprentissage (CELESTE); Inria Saclay - Ile de France; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO); Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS); University of Bristol Bristol; EDF R&D (EDF R&D); EDF (EDF)
    • بيانات النشر:
      HAL CCSD
      Elsevier
    • الموضوع:
      2022
    • نبذة مختصرة :
      International audience ; In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are a) a general and formal introduction to the multi-resolution modelling approach, b) a discussion on modelling approaches at different resolutions implemented via Generalised Additive Models and Neural Networks and c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low-and high-resolution alternatives.
    • Relation:
      hal-03469721; https://inria.hal.science/hal-03469721; https://inria.hal.science/hal-03469721/document; https://inria.hal.science/hal-03469721/file/Preprint.pdf
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.6506322E