One issue that often gets lost during discussions of emerging technologies is how many of them might already be in use to revolutionize industries. For example, Artificial Intelligence (AI) is making waves in ways that could not even be imagined five years ago. One industry which is feeling the impact of AI is wind power: wind farm developers are relying on the technology to make smart wind smarter. Applications include demand forecasting, planning and mapping wind farms, scheduling maintenance and even improving performance of materials.
One practical example of AI in managing wind farms is in eastern Colorado, at a wind farm owned by Excel Energy. These farms are linked to servers at the National Center for Atmospheric Research (NCAR) in Boulder. The link allows the sharing of information from satellites as well as local sensors, including those located at other wind farms in the area. This information is then fed into the NCAR’s servers to lower the cost of generation.
The U.S. Energy Information Administration, a government agency which researches trends in energy markets, reports that the levelized cost of electricity of wind power is roughly 10 percent lower than conventional natural gas generation and nearly half the cost of nuclear power.
This increased efficiency is a big reason why one kilowatt of wind power is more efficient than almost every other energy generation source on the market today. It has also helped to drive down the cost of wind energy by nearly 40 percent1 since 2000.
While the trend toward efficiency started before the implementation of advanced technologies such as AI, analytics, and the Internet of Things (IoT), these new technologies have doubled the amount of wind power generated in the United States since 2009.
One of the reasons for the tremendous growth is improved forecasting. This starts in the planning stages for a new wind farm. Granted, the need to model wind forecasts have been around since the dawn of wind farms. Yet tools such as AI, big data and analytics help to improve the accuracy of forecasting by considering information gleaned from other wind farms in the area, and by mining years of weather forecasts. Some of these forecasts are updated on the hour. Models are then built that not only give the average conditions, but also consider seasonality and the impact of climate change. Taken together, this data helps developers to build a more accurate assessment of the feasibility of one site over another.
Another way AI helps is by assisting in the mapping of a wind farm. This includes providing recommendations on which generator array will provide optimal results once a prospective site goes live. Such mapping can also help project managers to determine sequencing and equipment choices.
The impact of AI and analytics does not end at the planning stage. Once a wind farm goes live, advanced technologies are helping operators to better manage issues related to intermittency. This includes forecasting wind conditions and matching production capacity with demand. This goes beyond theory: a team of scientists in India recently published a paper on the impact of learning algorithms to develop dynamic forecasting models. Their findings not only investigated results from India, but also considered real-world applications in China and the United States.
Meanwhile, the Global Wind Energy Council has published a release from SentientScience which highlighted other uses for advanced technologies in the management of wind farms. Based on the release, these technologies ‘can also be crucial to insurance and warranty negotiations by having the data needed to identify who pays.’ This approach also motivates manufacturers of generators and other components to dramatically increase the performance characteristics of their products.
The release went on to state that AI and related technologies offer the opportunity to make wind power even more sustainable, with or without incentives. This would be a welcomed development, as it would highlight the gap between traditional energy generation technologies, which often rely on tax incentives and other market deviations to remain profitable, and alternative sources – especially wind power.
Don���t take our word for it. Excel Energy had previously predicted its wind power installations would amount to 10 percent of all power generated in the region at an additional cost of $1.5 billion. However, the use of AI and other technologies helped the company to get nearly 30 percent of its power from renewable sources, at a fraction of the cost.
One added benefit of this shift in generating sources is the ability to dramatically reduce Excel’s carbon footprint whilst expanding its service. This is part of the reason why the American Wind Energy Association announced in February that the United States is now the global leader in wind energy production. This advancement highlights how quickly the gap has closed between the United States and European countries when it comes to the adoption of wind power.
As shown, AI and other technologies have made the leap from the laboratory to the market, and wind power is one industry that has benefited. Not only has the technology helped to improve the placement, planning, and construction of wind farms. But it is also being used to improve efficiency. This is making a smart choice for power generation seem even smarter.
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1Source: Wood, D. United States Department of Energy. 6 Charts that Will Make You Optimistic About America’s Clean Energy Future. 28 September 2016. https://goo.gl/tFciKX. Retrieved 5 October 2016.
2Source: Bullis, K. MIT Technology Review. Smart Wind and Solar Power: Big data and artificial intelligence are producing ultra-accurate forecasts that will make it feasible to integrate much more renewable energy into the grid. No Date. https://goo.gl/LMBYcW. Retrieved 10 November 2016.