Leveraging AI Technology for Advancements in Wind Power

Authors

  • Yizhe Xia

DOI:

https://doi.org/10.61173/cgtf5f77

Keywords:

Artificial intelligence, Wind power, Energy production

Abstract

In a world where energy policies are becoming more proactive, and the rapid advancements in AI technology drive market demand, the shift towards energy is expected to peak in the 2030s. As of May 2023, nonrenewable energy sources like fossil fuel-based power generation still contribute to 61.8% of the production, while renewable energy sources make up 38.2%.‎ Although fossil fuels will continue to play a role in our energy mix, renewable energy has potential. This research paper delves into the impact of Artificial Intelligence (AI) on wind power generation. Given the need for energy solutions to combat climate change, this study examines how AI technology can be integrated into the wind power sector. The paper provides an overview of the situation, highlighting challenges and opportunities for AI to enhance wind power generation, particularly in areas such as forecasting and maintenance. As wind power continues to gain importance, embracing AI offers a chance to optimize operations and reshape the energy landscape. Ultimately, this research aims to improve efficiency and expedite the transition towards a future in energy production. 

References

predictive capabilities in wind energy is undeniable. As [1] Warden T, Oswald F, Roth E M. The National Academies technology evolves and collaboration across disciplines Board on Human System Integration (BOHSI) Panel: Promise, deepens, the wind power sector is poised to embrace AI- Progress and Challenges of Leveraging AI Technology in driven advancements, accelerating the transition towards a Healthcare. Proceedings of the Human Factors and Ergonomics more sustainable energy landscape. Society Annual Meeting, 2020, 64(1): 2124-2128. [2] Ernst B, Wan Y-H, Kirby B. Short-Term Power Fluctuation 5. Conclusion of Wind Turbines: Analyzing Data from the German 250-MW In the dynamic nexus of wind power and artificial Measurement Program from the Ancillary Services Viewpoint. intelligence, the trajectory of energy generation is set United States: N. p. 1999. Web. for a transformative journey. As demonstrated in this [3] Farhad E, Abolhasan K. Application of machine learning exploration, AI’s integration into wind power holds for wind energy from design to energy-Water nexus: A Survey. Energy Nexus, 2021, 2: 100011.

[4] Łukasz P, Sandy P. Harrison, Modelling and prediction of and Prospects. IEEE Access, 2021, 9: 102460-102489. wind damage in forest ecosystems of the Sudety Mountains, SW [8] David I. Chapter 15-Wind Energy, Editor(s): Trevor M. Poland. Science of The Total Environment, 2022, 815: 151972. Letcher, Future Energy (Second Edition), Elsevier, 2014, pp. [5] Cyril V, Marc M, Christophe P, Marie-Laure N. Numerical 313-333. weather prediction (NWP) and hybrid ARMA/ANN model to [9] Yang L, Wang W. Wind power forecasting considering data predict global radiation. Energy, 2012, 39: 341-355. privacy protection: A federated deep reinforcement learning [6] Godinho M, Castro R. Comparative performance of approach. Applied Energy, 2023, 329: 120291. AI methods for wind power forecast in Portugal. Wind [10] Tomonobu S. Shaping the future of sustainable energy Energy, 2021, 24: 39-53. through AI-enabled circular economy policies. Circular [7] Lipu A. Artificial Intelligence Based Hybrid Forecasting Economy, 2023, 2: 100040. Approaches for Wind Power Generation: Progress, Challenges

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Published

2024-01-03