Daily peak electrical load forecasting with a multi-resolution approach
| Autor: | Yvenn Amara-Ouali, Matteo Fasiolo, Yannig Goude, Hui Yan |
|---|---|
| Přispěvatelé: | 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) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning [STAT.AP]Statistics [stat]/Applications [stat.AP] Neural Networks [SPI.NRJ]Engineering Sciences [physics]/Electric power Generalised Additive Models Automated Feature Engineering Smart Grids [SDE.ES]Environmental Sciences/Environmental and Society Machine Learning (cs.LG) Methodology (stat.ME) Peak load Forecasting [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Multi-resolution Business and International Management [STAT.ME]Statistics [stat]/Methodology [stat.ME] Statistics - Methodology |
| Zdroj: | International Journal of Forecasting International Journal of Forecasting, 2022 |
| ISSN: | 0169-2070 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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