Artificial intelligence based hybrid forecasting approaches for wind power generation: Progress, challenges and prospects
Open accessAbstract
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.
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Poor data quality (sensor noise, incomplete/heterogeneous datasets) is a primary bottleneck, leading to skewed AI predictions and hampering applications like renewable forecasting.
“Poor data quality (sensor noise, incomplete/heterogeneous datasets) is a primary bottleneck, leading to skewed AI predictions [5,11,19] and hampering applications like renewable forecasting [4,10].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [4]
Overfitting to noisy or unrepresentative data limits real-world utility.
“Overfitting to noisy or unrepresentative data (Section 3.1.1)limits realworld utility [4,27].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [4]
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“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting
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A review of wind power forecasting & prediction
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Flagged first, then by citations
Research works printed but not linked: 1
- MD. SAZAL MIAH received the B.Sc. degree in electrical and electronic engineering from the University of Asia Pacific, Bangladesh, in 2018. He is currently pursuing the M. Eng. degree in nanotechnology with the Asian Institute of Technology, Bangkok, Thailand. His research interests include battery storage systems, opti- mization, electrode design, artificial intelligence, and biomedical signal processing using various machine learning algorithms. He received the pres- tigious His Majesty the Kings Scholarship (Royal Thai government Schol- arship) in 2020/2021 for the M.Eng. degree. M. A. HANNAN (Senior Member, IEEE) received the B.Sc. degree in electrical and electronic engi- neering from the Chittagong University of Engi- neering and Technology, Chittagong, Bangladesh, in 1990, and the M.Sc. and Ph.D. degrees in elec- trical, electronic, and systems engineering from Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia, in 2003 and 2007, respectively. He is currently a Professor of intelligent systems with the Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga Nasional, Malaysia. He has more than 28 years of industrial and academic experience. He is the author or coauthor of around 300 articles published in international journals and conference proceedings. His research interests include intelligent controllers, power electronics, hybrid vehicles, energy storage systems, image and signal processing, and artificial intelligence. He received number of gold awards for his innovative research in ITEX, MTE, INNOFEST, SIIF, and PERINTIS.
- Authors
- Lipu, M. S. H., Miah, M. S., Hannan, M. A., Hussain, A., Sarker, M. R., Ayob, A., Saad, M. H. M., & Mahmud, M. S.
- Affiliations
- National University of Malaysia (MY)
- Asian Institute of Technology (TH)
- Universiti Tenaga Nasional (MY)
- Nagoya University (JP)
- ORCID
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 102460-102489
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3185655613
- Cited by
- 121
- References
- 137
- Citations by year
- 2026: 27 · 2025: 35 · 2024: 27 · 2023: 19 · 2022: 11 · 2021: 2
- FWCI
- 4.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Universiti Kebangsaan Malaysia (DIP-2018-020)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- wind power forecasting, hybrid forecasting model, artificial intelligence, wind energy, power system operation, variable renewable energy, data preprocessing, feature selection, hyperparameter optimization, activation functions, forecast evaluation
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