Powering Electricity Forecasting with Transfer Learning
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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/32533 |
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| _version_ | 1864513436301393920 |
|---|---|
| author | Kamalov, Firuz |
| author2 | Sulieman, Hana Moussa, Sherif Reyes, Jorge Avante Safaraliev, Murodbek |
| author2_role | author author author author |
| author_facet | Kamalov, Firuz Sulieman, Hana Moussa, Sherif Reyes, Jorge Avante Safaraliev, Murodbek |
| author_role | author |
| dc.creator.none.fl_str_mv | Kamalov, Firuz Sulieman, Hana Moussa, Sherif Reyes, Jorge Avante Safaraliev, Murodbek |
| dc.date.none.fl_str_mv | 2024 2025-12-08T07:45:55Z 2025-12-08T07:45:55Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Kamalov, F.; Sulieman, H.; Moussa, S.; Avante Reyes, J.; Safaraliev, M. Powering Electricity Forecasting with Transfer Learning. Energies 2024, 17, 626. https://doi.org/10.3390/en17030626 1996-1073 https://hdl.handle.net/11073/32533 10.3390/en17030626 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | https://doi.org/10.3390/en17030626 |
| dc.subject.none.fl_str_mv | Electricity forecasting Transfer learning Electricity generation NBEATS Deep learning Medium-term |
| dc.title.none.fl_str_mv | Powering Electricity Forecasting with Transfer Learning |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| format | article |
| id | aus_981bdc4eea88c3c331553a027bc64591 |
| identifier_str_mv | Kamalov, F.; Sulieman, H.; Moussa, S.; Avante Reyes, J.; Safaraliev, M. Powering Electricity Forecasting with Transfer Learning. Energies 2024, 17, 626. https://doi.org/10.3390/en17030626 1996-1073 10.3390/en17030626 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32533 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Powering Electricity Forecasting with Transfer LearningKamalov, FiruzSulieman, HanaMoussa, SherifReyes, Jorge AvanteSafaraliev, MurodbekElectricity forecastingTransfer learningElectricity generationNBEATSDeep learningMedium-termAccurate 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.American University of SharjahMDPI2025-12-08T07:45:55Z2025-12-08T07:45:55Z2024Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfKamalov, F.; Sulieman, H.; Moussa, S.; Avante Reyes, J.; Safaraliev, M. Powering Electricity Forecasting with Transfer Learning. Energies 2024, 17, 626. https://doi.org/10.3390/en170306261996-1073https://hdl.handle.net/11073/3253310.3390/en17030626en_UShttps://doi.org/10.3390/en17030626oai:repository.aus.edu:11073/325332025-12-08T11:41:31Z |
| spellingShingle | Powering Electricity Forecasting with Transfer Learning Kamalov, Firuz Electricity forecasting Transfer learning Electricity generation NBEATS Deep learning Medium-term |
| status_str | publishedVersion |
| title | Powering Electricity Forecasting with Transfer Learning |
| title_full | Powering Electricity Forecasting with Transfer Learning |
| title_fullStr | Powering Electricity Forecasting with Transfer Learning |
| title_full_unstemmed | Powering Electricity Forecasting with Transfer Learning |
| title_short | Powering Electricity Forecasting with Transfer Learning |
| title_sort | Powering Electricity Forecasting with Transfer Learning |
| topic | Electricity forecasting Transfer learning Electricity generation NBEATS Deep learning Medium-term |
| url | https://hdl.handle.net/11073/32533 |