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http://elar.urfu.ru/handle/10995/141716
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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Файл | Описание | Размер | Формат | |
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2-s2.0-85184657684.pdf | 571,07 kB | Adobe PDF | Просмотреть/Открыть |
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