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Over the past ten years, wind energy has grown to be a significant contributor to global carbon-free energy. However, this energy source remains unreliable due to the erratic and variable nature of the wind.
Today, the ever-growing wind power industry requires significant economical, technological, and logistical improvements in the energy transition to renewable resources.
External elements like wind speed, temperature, direction, and pressure all influence wind power generation. Soon, AI and machine learning will improve wind energy’s reliability by providing accurate wind and weather forecasts. Wind farm operators will soon be able to rely on the deep learning-based prediction methods of artificial intelligence. This helps determine the wind power output of their plants accurately.
Recent years have seen a rapid development of machine learning techniques, which help predict wind energy production better. When it comes to feature extraction and model generalization, machine learning has outperformed conventional statistical prediction models.
Aside from accurate forecasting, there are other methods that artificial intelligence and machine learning could assist the wind industry: from lowering maintenance costs to monitoring demand and consumption.
Continue reading to learn more about the various benefits that machine learning provides and the industry leaders who are working tirelessly to make clean energy more reliable and accessible.
A Brief Introduction To Machine Learning
Artificial intelligence (AI) that uses a machine-learning approach trains computers to think like people do by learning from and improving upon prior experiences. It uses data exploration and pattern recognition to operate with little to no supervisory control and human intervention.
These systems learn, grow, alter, and expand on their own when presented with new data. In other words, machine learning entails computers discovering valuable knowledge independently without being told where to look. Instead, they achieve this by utilizing algorithms that continually learn from provided data.
Machine learning models can automate almost any task with data-defined patterns or rules. For example, with grid-connected wind farms, computers can analyze current and historical weather data patterns to predict the plant’s actual power generation reliably.
Types Of Machine Learning
There are three types and categories of machine learning: supervised, unsupervised, and reinforcement.
Supervised machine learning
Supervised machine learning models trained with labeled data sets, allowing them to learn and become more accurate over time. For example, programmers might train an algorithm with images of dogs and other objects that are humans. The computer would then learn how to recognize pictures of dogs on its own.
This type of machine learning is the most used today as it provides efficient ways to train systems without taking too much time.
Unsupervised machine learning
Unsupervised machine learning is when a program searches for patterns in an unsupervised data set. Unsupervised machine learning can identify patterns or trends that aren’t actively sought after. For instance, an unsupervised machine learning software could examine data sets from online sales and find various customer demographics making purchases.
Reinforcement machine learning
By setting up a reward system, reinforcement machine learning teaches computers to choose the best course of action through trial and error. Over time, the system will understand what actions to take. By letting the machine know when it made the appropriate choices, reinforcement learning algorithms can train autonomous vehicles to drive.
Neural Networks
A popular and distinct class of machine learning techniques is neural networks. Artificial neural networks are modeled after the human brain, which has layers of millions of interconnected processing nodes.
An artificial neural network comprises interconnected cells or nodes. Each cell processes inputs and generates an output sent to neighboring neurons. Labeled data sets are transmitted across the entire network consisting of nodes, or cells, where each cell has a specific purpose. For example, the various nodes of a neural network trained to determine if an image contains a cat or not would evaluate the data and produce an output that indicates whether a picture has a cat.
Deep Learning
Deep learning, a neural network with three or more input layers, is a subset of machine learning. These deep learning neural networks make an effort to mimic how the human brain functions. Even though they fall far short of being able to match the brain, these networks are still capable of learning from vast volumes of data.
For instance, in an image identification system, one layer of the neural network may be able to identify specific facial characteristics, such as the eyes, nose, or mouth. In contrast, a different layer may be able to determine whether those features exist in a way that suggests the presence of a face.
How Machine Learning Can Benefit The Wind Industry
Artificial Intelligence Can Forecast The Energy Output Of Wind Farms
Wind farm owners can use highly precise data on wind speed and direction from machine learning to further accurately predict how much energy the plant will produce, allowing for better performance for wind farms.
From an economic perspective, precise output forecasting can prove crucial for an efficient and financially viable operation.
Machine Learning Reduces Operating And Maintenance Costs
The turbines used at wind farms, from regular to variable speed wind turbines, are known for their relatively short lifespans. Wind turbines have an average working life of 20 to 25 years and are unlikely to last longer than this because of the extreme loads they carry.
Particularly vulnerable to damage are the blades of wind turbines. These blades wear because of their high wear and tear as moving parts. Birds and other potential projectiles can also harm them, leaving them inoperable.
Blades and gearboxes are often already in need of replacement after ten years of service; therefore, they are unlikely to last another decade. A single wind turbine costs $200,000 to break down, not including any profit from selling or recycling materials, which is time-consuming and not usually cost-effective.
Thanks to AI and machine learning, it is possible to estimate the potential downtime of components correctly. As a result, the wind energy production of the plant rises, component replacement costs drop and extend the overall life of the plant’s wind turbines.
A reliable condition monitoring strategy effectively lowers operating and maintenance expenditures by predicting problems before they occur. This monitoring strategy involves the observation of wind turbine components with the hopes of detecting early signs of wear or failure.
With the development of sensors and signal-processing systems, alongside machine learning models, condition monitoring for wind turbines has become more rapid and efficient. The data from the wind turbines can alert operators if the system detects any faults in the blades or electrical failures.
Artificial Intelligence Can Monitor Energy Consumption And Demand
Operators can use artificial intelligence to analyze the data from smart power grids, allowing operators to monitor how much power their customers consume. These automated power grids can also control the outgoing electricity from power plants.
The ability to monitor and study the energy consumption of large cities can prove helpful, primarily to wind farm operators, as wind turbines sometimes produce too little or too much power. Wind farm operators can use the data gathered by artificial intelligence to make the appropriate changes to power distribution in urban and rural areas.
Although smart power grids may provide a convenient way of tracking energy demand and consumption, a centralized data collection system can become a prime target for hackers capable of stealing valuable consumer information and disrupting the power system.
AI Advancements In The Wind Industry
DeepMind Machine Learning Algorithms
In 2018, DeepMind and Google began using machine learning algorithms on 700 megawatts of wind energy production capacity in the central United States to find a solution to wind energy’s unpredictable and unreliable nature.
Google set up the DeepMind system to anticipate wind power output 36 hours ahead of actual generation using a neural network trained on publicly accessible weather forecasts and historical turbine data.
Despite continuously adjusting the algorithm, Google believes that its use of machine learning in wind farms has shown positive outcomes. The results that the system has shown so far imply that machine learning can raise the value and predictability of wind energy. In 2019, Google claimed that machine learning had increased the value of their wind energy by about 20 percent.
The company gave little information regarding the general accuracy of the system’s forecasts up to this point. Still, Google and DeepMind hope to use machine learning to make wind power more dependable in the future.
MIT’s Machine Learning Model
Massachusetts Institute of Technology (MIT) used machine learning to develop a technique that forecasts changes in wind speed more quickly over an extended period. The approach makes it simpler for emerging renewable energy companies to find potential wind projects.
With only three months of historical data for a specific wind farm location, the MIT team could predict wind speeds over the two years more accurately than previous models could with eight months of data.
Since then, the researchers have refined their model by testing several techniques for predicting joint distributions. Additional data from the Museum of Science indicates that their improved strategy may double the precision of their estimates.
Vaisala Wind Energy Forecaster
The Vaisala Forecaster for Wind Energy offers incredibly accurate wind forecasts to the wind energy sector to properly manage investments, lower risk, and gain a competitive edge in the market. They also provide proxy data in areas where it is impossible to conduct direct observations.
Vaisala’s Forecaster provides site-specific and regional prediction tools for asset owners, project managers, wind farm operators, and energy traders, allowing them to make data-driven decisions. They provide:
- Data on aggregate wind power.
- Generating capacity.
- The potential generation of individual turbines.
These models foresee wind energy generation using several data sets based on complex fluid dynamics and incorporating data from long-term wind measurements.
Vaisala uses specialized Numerical Weather Prediction models, publicly available weather forecasts, and machine learning algorithms to create accurate wind energy predictions. These Numerical Weather Prediction models use the atmosphere’s mathematical models to predict the weather based on available current and historical data on weather conditions.
Rutgers University’s Algorithm
A machine-learning method was developed by Rutgers University researchers, utilizing physics-based simulators and historical data on the weather to forecast offshore wind generation. Furthermore, they created a sensitivity analysis methodology to identify and foresee the critical elements that influence the power generated by wind turbines.
The University’s algorithm integrates results from a physics-based simulator with actual data on weather conditions gathered from a set of buoys installed off the coast of New Jersey. These buoys are closely related to at least three potential offshore wind farms.
Machine Learning Is Leading Wind Energy Into The Future
The demand for renewable energy sources like wind and solar power is rapidly rising as the world’s supply of conventional energy sources runs out. Among all available clean power sources, the wind has the most potential to replace traditional energy as it is practically infinite.
Undoubtedly, the energy industry must undergo a drastic transformation. Climate change is an urgent issue that the international community must address immediately with effective long-term solutions. Wind energy remains one of the best choices for sustainable power generation.
As such, the international community is making every effort to achieve an adequate energy transition to renewable resources and reduce the carbon emissions produced by fossil fuels and traditional energy.
The industry’s technological advances of the last decade, paired with the various advantages that wind energy offers, have made wind power an excellent candidate to replace coal, oil, and natural gases as the planet’s primary energy resource. With the help of AI and machine learning, wind energy will continue to become cheaper, more efficient, and reliable, making a global energy transition to renewable sources viable.