Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects
Artificial intelligence; Electric power generation; Stochastic systems; Weather forecasting; Wind power; Activation functions; Generalization performance; Influential factors; Learning capabilities; Performance analysis; Stochastic characteristic; Training algorithms; Wind power forecasting; Electri...
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2023
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my.uniten.dspace-265182023-05-29T17:11:26Z Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects Lipu M.S.H. Miah M.S. Hannan M.A. Hussain A. Sarker M.R. Ayob A. Saad M.H.M. Mahmud M.S. 36518949700 57226266149 7103014445 57208481391 57537703000 26666566900 7202075525 57220492528 Artificial intelligence; Electric power generation; Stochastic systems; Weather forecasting; Wind power; Activation functions; Generalization performance; Influential factors; Learning capabilities; Performance analysis; Stochastic characteristic; Training algorithms; Wind power forecasting; Electric power transmission networks Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation. � 2013 IEEE. Final 2023-05-29T09:11:26Z 2023-05-29T09:11:26Z 2021 Article 10.1109/ACCESS.2021.3097102 2-s2.0-85110853330 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110853330&doi=10.1109%2fACCESS.2021.3097102&partnerID=40&md5=b08744e41dfa01fa4c059d215402ca28 https://irepository.uniten.edu.my/handle/123456789/26518 9 9483904 102460 102489 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus |
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Artificial intelligence; Electric power generation; Stochastic systems; Weather forecasting; Wind power; Activation functions; Generalization performance; Influential factors; Learning capabilities; Performance analysis; Stochastic characteristic; Training algorithms; Wind power forecasting; Electric power transmission networks |
| author2 |
36518949700 |
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36518949700 Lipu M.S.H. Miah M.S. Hannan M.A. Hussain A. Sarker M.R. Ayob A. Saad M.H.M. Mahmud M.S. |
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Article |
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Lipu M.S.H. Miah M.S. Hannan M.A. Hussain A. Sarker M.R. Ayob A. Saad M.H.M. Mahmud M.S. |
| spellingShingle |
Lipu M.S.H. Miah M.S. Hannan M.A. Hussain A. Sarker M.R. Ayob A. Saad M.H.M. Mahmud M.S. Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects |
| author_sort |
Lipu M.S.H. |
| title |
Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects |
| title_short |
Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects |
| title_full |
Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects |
| title_fullStr |
Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects |
| title_full_unstemmed |
Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects |
| title_sort |
artificial intelligence based hybrid forecasting approaches for wind power generation: progress, challenges and prospects |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806428441724059648 |
| score |
13.222552 |