A survey on wind power forecasting with machine learning approaches
Journal article
Yang, Yang, Lou, Hao, Wu, Jinran, Zhang, Shaotong and Gao, Shangce. (2024). A survey on wind power forecasting with machine learning approaches. Neural Computing and Applications. 36, pp. 12753-12773. https://doi.org/10.1007/s00521-024-09923-4
Authors | Yang, Yang, Lou, Hao, Wu, Jinran, Zhang, Shaotong and Gao, Shangce |
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Abstract | Wind power forecasting techniques have been well developed over the last half-century. There has been a large number of research literature as well as review analyses. Over the past 5 decades, considerable advancements have been achieved in wind power forecasting. A large body of research literature has been produced, including review articles that have addressed various aspects of the subject. However, these reviews have predominantly utilized horizontal comparisons and have not conducted a comprehensive analysis of the research that has been undertaken. This survey aims to provide a systematic and analytical review of the technical progress made in wind power forecasting. To accomplish this goal, we conducted a knowledge map analysis of the wind power forecasting literature published in the Web of Science database over the last 2 decades. We examined the collaboration network and development context, analyzed publication volume, citation frequency, journal of publication, author, and institutional influence, and studied co-occurring and bursting keywords to reveal changing research hotspots. These hotspots aim to indicate the progress and challenges of current forecasting technologies, which is of great significance for promoting the development of forecasting technology. Based on our findings, we analyzed commonly used traditional machine learning and advanced deep learning methods in this field, such as classical neural networks, and recent Transformers, and discussed emerging technologies like large language models. We also provide quantitative analysis of the advantages, disadvantages, forecasting accuracy, and computational costs of these methods. Finally, some open research questions and trends related to this topic were discussed, which can help improve the understanding of various power forecasting methods. This survey paper provides valuable insights for wind power engineers. |
Keywords | Wind power prediction; Time series; Machine learning; Deep learning ; Large language models |
Year | 01 Jan 2024 |
Journal | Neural Computing and Applications |
Journal citation | 36, pp. 12753-12773 |
Publisher | Springer |
ISSN | 0941-0643 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00521-024-09923-4 |
Web address (URL) | https://link.springer.com/article/10.1007/s00521-024-09923-4 |
Open access | Open access |
Research or scholarly | Research |
Page range | 12753-12773 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
18 May 2024 | |
Publication process dates | |
Accepted | 28 Apr 2024 |
Deposited | 13 Nov 2024 |
Additional information | © The Author(s) 2024 |
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
Place of publication | United Kingdom |
https://acuresearchbank.acu.edu.au/item/910xx/a-survey-on-wind-power-forecasting-with-machine-learning-approaches
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Publisher's version
OA_Wu_2024_A_survey_on_wind_power_forecasting.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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