A review and taxonomy of wind and solar energy forecasting methods based on deep learning
Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-06-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546821000148 |
| _version_ | 1818936245161558016 |
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| author | Ghadah Alkhayat Rashid Mehmood |
| author_facet | Ghadah Alkhayat Rashid Mehmood |
| author_sort | Ghadah Alkhayat |
| collection | DOAJ |
| description | Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems. Deep learning's recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications. To facilitate further research and development in this area, this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works, the data pre-processing methods, deterministic and probabilistic methods, and evaluation and comparison methods. The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons. The current challenges in the field and future research directions are given. The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit, and in the third place Convolutional Neural Networks. We also find that probabilistic and multistep ahead forecasting methods are gaining more attention. Moreover, we devise a broad taxonomy of the research using the key insights gained from this extensive review, the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field. |
| first_indexed | 2024-12-20T05:33:00Z |
| format | Article |
| id | doaj.art-0e2cff924c0f40689d929850a0ecd5f3 |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-12-20T05:33:00Z |
| publishDate | 2021-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-0e2cff924c0f40689d929850a0ecd5f32022-12-21T19:51:41ZengElsevierEnergy and AI2666-54682021-06-014100060A review and taxonomy of wind and solar energy forecasting methods based on deep learningGhadah Alkhayat0Rashid Mehmood1Department of Computer Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaHigh Performance Computing Centre, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Corresponding author.Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems. Deep learning's recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications. To facilitate further research and development in this area, this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works, the data pre-processing methods, deterministic and probabilistic methods, and evaluation and comparison methods. The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons. The current challenges in the field and future research directions are given. The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit, and in the third place Convolutional Neural Networks. We also find that probabilistic and multistep ahead forecasting methods are gaining more attention. Moreover, we devise a broad taxonomy of the research using the key insights gained from this extensive review, the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.http://www.sciencedirect.com/science/article/pii/S2666546821000148Deep learningRenewable energy forecastingSolar energyWind energyTaxonomyHybrid methods |
| spellingShingle | Ghadah Alkhayat Rashid Mehmood A review and taxonomy of wind and solar energy forecasting methods based on deep learning Energy and AI Deep learning Renewable energy forecasting Solar energy Wind energy Taxonomy Hybrid methods |
| title | A review and taxonomy of wind and solar energy forecasting methods based on deep learning |
| title_full | A review and taxonomy of wind and solar energy forecasting methods based on deep learning |
| title_fullStr | A review and taxonomy of wind and solar energy forecasting methods based on deep learning |
| title_full_unstemmed | A review and taxonomy of wind and solar energy forecasting methods based on deep learning |
| title_short | A review and taxonomy of wind and solar energy forecasting methods based on deep learning |
| title_sort | review and taxonomy of wind and solar energy forecasting methods based on deep learning |
| topic | Deep learning Renewable energy forecasting Solar energy Wind energy Taxonomy Hybrid methods |
| url | http://www.sciencedirect.com/science/article/pii/S2666546821000148 |
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