Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges

Lu, Peng; Lin, Yi; Zhao, Yongning; Dai, Binhua; Pei, Ming; Tang, Yong

doi:10.1016/j.apenergy.2021.117446

Cited by 118 publications

(30 citation statements)

References 229 publications

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“…Thus, the artificial intelligence method has aroused wide attention around the world. In particular, references [9,10] thoroughly summarized and analyzed meta-heuristic algorithms-based modeling techniques for different photovoltaic (PV) modules. An extreme learning machine was used to implement rapid and precise modeling of PV modules [11] and solid oxide fuel cells [12] based on measurement data under various operation conditions.…”

Section: Accurate Modelingmentioning

confidence: 99%

Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets: Current States and燩erspectives

Yang¹,

Guo²,

Wang³

et al. 2022

Energy Engineering

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Section: Accurate Modelingmentioning

confidence: 99%

Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets: Current States and燩erspectives

Yang¹,

Guo²,

Wang³

et al. 2022

Energy Engineering

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“…An overview of the objectives of control and diagnosis and also wind-speed forecasting can be found in Refs. [14][15][16][17][18][19][20][21][22][23] . Wind-farm flow modeling with a data-driven approach is covered in this review article.…”

Section: Introductionmentioning

confidence: 99%

Data-driven fluid mechanics of wind farms: A review

Zehtabiyan-Rezaie,

Iosifidis,

Abkar

2022

Preprint

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With the growing number of wind farms over the last decades and the availability of large datasets, research in wind-farm flow modeling -one of the key components in optimizing the design and operation of wind farms -is shifting towards data-driven techniques. However, given that most current data-driven algorithms have been developed for canonical problems, the enormous complexity of fluid flows in real wind farms poses unique challenges for data-driven flow modeling. These include the high-dimensional multiscale nature of turbulence at high Reynolds numbers, geophysical and atmospheric effects, wake-flow development, and incorporating wind-turbine characteristics and wind-farm layouts, among others. In addition, data-driven wind-farm flow models should ideally be interpretable and have some degree of generalizability. The former is important to avoid a lack of trust in the models with end-users, while the most popular strategy for the latter is to incorporate known physics into the models. This article reviews a collection of recent studies on wind-farm flow modeling covering both purely data-driven and physics-guided approaches. We provide a thorough analysis of their modeling approach, objective, and methodology, and specifically focus on the data utilized in the reviewed works.

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“…As one of the renewable resources, wind energy is being used worldwide. Researchers from various countries have carried out lots of work on the current status and future of wind power in their countries [ 1 – 6 ]. Development of wind power from the viewpoints of political, social, and technical issues was analyzed in [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Considering the uncertainty of wind energy on grid connection, research work focuses on wind power prediction and how to do global dispatch planning and design by prediction results [ 6 , 21 , 22 ], including data model, physical model, and hybrid model [ 23 – 25 ]. The continuous development of artificial intelligence algorithms has further expanded the scope of its application to new areas [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Diurnal Modulation Characteristic of Wind Speed and Power Generation Revealed by Its Measured Data Processing

Wan

Yao

Ren

et al. 2022

Computational Intelligence and Neuroscience

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Atmospheric turbulence is an intrinsic factor that causes uncertainty of wind speed and its power generation by wind turbine. The research of atmospheric turbulence characteristics of wind farms can be used to reduce this uncertainty. In this paper, enough measurement data getting from actual wind farms is used for information processing to quantitatively analyze the daily variation of wind speed and its power output characteristics. Furthermore, the concept of spatiotemporal diurnal modulation characteristics of atmospheric turbulence is proposed with a global scope, which is an intrinsic property of wind. Besides the daily variation characteristics, the average hourly wind speed has a short-term modulation effect on its turbulence and provides a modulation characteristic on wind speed uncertainty. Moreover, the long-term modulation process is affected by seasonal and regional factors, indicating that it has spatiotemporal characteristics. This atmospheric turbulence characteristic has similar effects on characteristic description parameters. However, the characteristics description parameters of wind speed and wind power variation fail to reflect such intrinsic characteristics that are not affected by the spatiotemporal diurnal modulation characteristics of atmospheric turbulence. This indicates that they do not have diurnal characteristics. Finally, a time-varying model combined with the spatiotemporal diurnal modulation characteristics of wind speed and its power generation is discussed by applying on the evaluation of frequency control in power systems. It is shown that the results obtained by measured data processing could improve the power generation quality of large-scale wind power effectively.

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Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges

Cited by 118 publications

References 229 publications

Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets: Current States and燩erspectives

Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets: Current States and燩erspectives

Data-driven fluid mechanics of wind farms: A review

Spatiotemporal Diurnal Modulation Characteristic of Wind Speed and Power Generation Revealed by Its Measured Data Processing

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