AI in wind farms: The quest for smart wind production

Image credit

AI in wind farms: The quest for smart wind production


The Quantumrun Trends Platform will give you the insights, tools, and community to explore and thrive from future trends.



AI in wind farms: The quest for smart wind production

Subheading text
Harnessing the wind just got smarter with AI, making wind production even more reliable and cost-effective.
    • Author:
    • Author name
      Quantumrun Foresight
    • March 21, 2024

    Insight summary

    Artificial Intelligence (AI) is transforming the wind energy sector by making wind farms operate more efficiently and produce more energy. Through collaborations between leading technology companies and research institutions, AI is being used to optimize wind turbine performance and predict energy outputs, marking a significant shift in how renewable energy is managed and utilized. These efforts are making wind power more cost-effective and paving the way for a more sustainable and secure energy future.

    AI in wind farms context

    Artificial Intelligence is making significant strides in the wind energy sector, transforming how wind farms operate and enhancing their efficiency. In 2023, Massachusetts Institute of Technology (MIT) researchers developed predictive models and employed supercomputer simulations alongside real-life data from wind farms, such as those located in northwest India, to increase the energy production of wind turbines. These advancements came at a time when the Global Wind Energy Council highlighted the wind power market's cost-competitiveness and resilience, with a notable surge in installations, particularly in China and the US.

    In 2022, Vestas Wind Systems collaborated with Microsoft and on a proof of concept focused on wake steering—a technique aimed at increasing the energy output from wind turbines. It involves adjusting the angles of turbines to minimize the aerodynamic interference between them, essentially reducing the "shadow effect" that can decrease the efficiency of downstream turbines. By leveraging AI and high-performance computing, Vestas optimized this process, potentially recapturing energy that would otherwise be lost due to the wake effect. 

    Another utility company, ENGIE, collaborated with Google Cloud in 2022 to optimize the value of wind power in short-term power markets, leveraging AI to predict wind power output and make more informed decisions about energy sales. This approach signifies a leap in maximizing the output from wind farms and exemplifies the practical application of AI in solving complex environmental and engineering challenges. With wind power set to play a crucial role in the global energy mix, as indicated by the International Energy Agency's projections for 2050, initiatives like these are critical. 

    Disruptive impact

    This shift toward more intelligent energy systems allows operators to adjust to changing weather conditions in real-time, optimizing power output and reducing waste. For consumers, this means a more stable and potentially lower-cost energy supply as providers can reduce operational costs and pass these savings on to consumers. Furthermore, the improved efficiency of wind farms could lead to a broader acceptance of renewable energy, encouraging more individuals to support or invest in green energy solutions.

    Companies investing in renewable energy technologies can expect a return on investment through increased energy production and operational efficiencies. This trend encourages businesses across various sectors to consider renewable energy not only as an ethical choice but as a financially viable one. Additionally, companies specializing in AI and data analysis will find new opportunities in the renewable energy sector, leading to innovations in how data is used to optimize energy production. This symbiotic relationship between the tech and renewable energy industries could accelerate the development of new solutions for energy management and sustainability.

    For governments, the long-term impact of AI-enhanced wind farms represents a substantial step towards meeting climate goals and transitioning to a low-carbon economy. By supporting the development and implementation of AI in renewable energy, governments can increase their countries' energy security, reduce dependence on imported fuels, and create high-tech jobs in the green economy. Moreover, AI's data-driven insights can help policymakers understand energy patterns better and make informed decisions on infrastructure and investments. 

    Implications of AI in wind farms

    Wider implications of AI in wind farms may include: 

    • A reduction in operational costs for wind farms through AI, making renewable energy more competitive against traditional sources.
    • The development of new educational curricula that emphasize AI skills in renewable energy, addressing the growing demand for a skilled workforce.
    • The acceleration of technological innovation in wind turbine design and operation as AI identifies new optimization strategies.
    • A shift in labor market demands, favoring professionals with expertise in AI, renewable energy, and environmental science.
    • The government implementing incentives for AI integration in renewable energy projects to achieve carbon neutrality goals faster.
    • An improvement in grid management and stability as AI optimizes the distribution of wind-generated power in real-time.
    • The emergence of new business models in the energy sector, centered around AI-driven data services and analytics for wind farms.
    • A heightened focus on cybersecurity measures within the renewable energy sector to protect AI systems from potential threats.

    Questions to consider

    • How could the job market evolve with the increasing need for AI skills in the renewable energy sector?
    • How might government policies on renewable energy and AI influence your local economy and environment in the next five years?

    Insight references

    The following popular and institutional links were referenced for this insight: