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Название: Powering Electricity Forecasting with Transfer Learning
Авторы: Kamalov, F.
Sulieman, H.
Moussa, S.
Avante, Reyes, J.
Safaraliev, M.
Дата публикации: 2024
Издатель: Multidisciplinary Digital Publishing Institute (MDPI)
Библиографическое описание: Kamalov, F., Sulieman, H., Moussa, S., Reyes, J. A., & Safaraliev, M. (2024). Powering Electricity Forecasting with Transfer Learning. Energies, 17(3), [626]. https://doi.org/10.3390/en17030626
Аннотация: Accurate forecasting is one of the keys to the efficient use of the limited existing energy resources and plays an important role in sustainable development. While most of the current research has focused on energy price forecasting, very few studies have considered medium-term (monthly) electricity generation. This research aims to fill this gap by proposing a novel forecasting approach based on zero-shot transfer learning. Specifically, we train a Neural Basis Expansion Analysis for Time Series (NBEATS) model on a vast dataset comprising diverse time series data. Then, the trained model is applied to forecast electric power generation using zero-shot learning. The results show that the proposed method achieves a lower error than the benchmark deep learning and statistical methods, especially in backtesting. Furthermore, the proposed method provides vastly superior execution time as it does not require problem-specific training. © 2024 by the authors.
Ключевые слова: DEEP LEARNING
ELECTRICITY FORECASTING
ELECTRICITY GENERATION
MEDIUM-TERM
NBEATS
TRANSFER LEARNING
ELECTROPHYSIOLOGY
ENERGY RESOURCES
FORECASTING
LEARNING SYSTEMS
LONG SHORT-TERM MEMORY
TIME SERIES
TIME SERIES ANALYSIS
'CURRENT
DEEP LEARNING
ELECTRICITY FORECASTING
ELECTRICITY-GENERATION
ENERGY PRICES
EXISTING ENERGIES
MEDIUM TERM
NEURAL BASE EXPANSION ANALYSE FOR TIME SERIES
TIMES SERIES
TRANSFER LEARNING
POWER GENERATION
URI: http://elar.urfu.ru/handle/10995/141716
Условия доступа: info:eu-repo/semantics/openAccess
cc-by
Идентификатор SCOPUS: 85184657684
Идентификатор WOS: 001159147500001
Идентификатор PURE: 52643244
ISSN: 1996-1073
DOI: 10.3390/en17030626
Сведения о поддержке: American University of Sharjah, AUS, (FRG22-C-S60); American University of Sharjah, AUS
This research was funded by the Open Access Program from the American University of Sharjah (AUS) and the AUS Faculty Research Grant FRG22-C-S60.
Карточка проекта РНФ: American University of Sharjah, AUS, (FRG22-C-S60); American University of Sharjah, AUS
This research was funded by the Open Access Program from the American University of Sharjah (AUS) and the AUS Faculty Research Grant FRG22-C-S60.
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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