AI-based wind power forecasting and its impact on maintenance scheduling optimization
Wind Power Forecasting
As the world shifts towards renewable energy sources, wind power has emerged as a leading player in the race to reduce our reliance on fossil fuels. With the global wind power capacity reaching 664 gigawatts (GW) in 2020, the wind industry has seen unprecedented growth in the last few decades. Wind turbines may be a cleaner energy source, but they also present unique maintenance and optimization challenges. That’s where AI-based wind power forecasting comes in.
Unlocking a world of efficient wind power
Artificial intelligence (AI) has revolutionized the way we approach renewable energy. Wind power is rapidly becoming one of the most important forms of energy in generating electricity and its environmental benefits. Only in the US, wind power accounted for 32% of energy capacity growth in 2021, employing 120,000 people and powering the equivalent of 40 million homes.
Gone are the days of relying on traditional forecasting methods, such as analyzing data from weather stations or satellite imagery. AI-based forecasting uses machine learning techniques to analyze various data sources, including real-time wind speed, temperature and direction, and historical data on wind patterns. These sophisticated AI algorithms are designed to learn and improve their performance without explicit programming, allowing them to analyze immense amounts of data and uncover complex patterns that can accurately forecast wind power output. It has been estimated that AI-based wind power forecasting can reduce the cost of wind energy by up to 30%.
The key to optimizing and maintenance scheduling!
Several different AI methods and industrial techniques are used in wind maintenance schedules. Machine learning algorithms are used to analyze large datasets and identify patterns and trends that can be used to make predictions. Deep learning algorithms detect more complex patterns, allowing for more accurate predictions. Industrial techniques used in wind maintenance scheduling include condition monitoring, predictive maintenance, and preventive maintenance. Condition monitoring is the use of various sensors to monitor a turbine’s condition, allowing for the detection of potential problems. Predictive maintenance uses AI algorithms to identify potential problems before they become an issue, allowing for proactive maintenance scheduling. Preventive maintenance involves using scheduled maintenance activities to reduce the likelihood of future problems.
Wind power forecasting enables operators to proactively address potential issues, ensuring smooth functioning of wind turbine power systems. By planning maintenance and ensuring turbine readiness, disruptions to the power grid can be minimized, meeting consumer energy demands. With accurate forecasting, wind turbine power systems can run efficiently and reliably, keeping the lights on for all.
AI-based predictive maintenance systems are leading the way as the wind energy sector innovates to stay ahead of the curve.
GE’s Turbine Health Monitoring System (THMS) utilizes AI to detect anomalies and predict performance degradation, enabling precise maintenance and repairs. Siemens Gamesa’s SWFS employs AI to forecast wind power generation in detail, using turbine and weather station data.
AI-Based Startups Leading the Way
As the demand for renewable energy continues to rise, AI-based wind power forecasting is becoming increasingly important. WindAI, a Danish startup, leads with an AI platform that predicts wind farm output by leveraging weather forecasts, sensor data, and satellite imagery. They tailor their platform to meet the needs of both small and large wind power plants. The company’s software leverages sensor data from the turbine and meteorological information such as wind velocity and atmospheric conditions to devise an optimal maintenance schedule for wind turbines. This proactive approach enables wind farm operators to schedule maintenance at opportune times rather than reacting to unforeseen breakdowns. The software self-identifies malfunctions, streamlining maintenance and minimizing disruptions by identifying root causes for turbine operations.
US startup Windward Engineering pioneers wind power systems, offering services from resource assessment to turbine design and manufacturing. By harnessing the power of machine learning, the company can precisely forecast wind resource availability and power generation with a remarkable 95% accuracy rate, allowing Windward Engineering to optimize their maintenance schedules and prevent costly breakdowns, ensuring their power plants run at maximum efficiency. Windward Engineering is leading the way in sustainable wind energy with wind resource assessments, maintenance, and repair services.
Major Players in AI-Based Wind Power Forecasting
EDF Renewables, another key player in the renewable field, helps customers develop wind energy projects, including site selection, financing, maintenance services and construction management. It has developed an AI tool called WindFlex that can predict wind patterns up to 72 hours in advance. This allows the company to optimize the operation of their wind farms and reduce maintenance needs.
In addition to startups, several established companies also leverage AI-based wind power forecasting to optimize their operations. For instance, GE Renewable Energy has developed an AI-based wind power forecasting platform called GE Predix. The platform utilizes data from various sources, such as weather forecasts, sensor data, and satellite imagery, to predict the future output of wind power plants. GE Predix is designed to be highly accurate and can predict the output of wind power plants with an accuracy of up to 95%.
Another major player in the AI-based wind power forecasting space is Siemens Gamesa Renewable Energy. The company has developed an AI-based platform called SGRE Wind Power Forecast that utilizes data from various sources, such as weather forecasts, sensor data, and satellite imagery, to predict the future output of wind power plants. The platform is designed to be scalable and can be used by small and large wind power plants alike.
In addition to these startups and established companies, there are several other players in the AI-based wind power forecasting space, such as Vestas, EON, and Vattenfall. These companies also leverage AI-based technologies to optimize their wind energy operations and reduce downtime.
AI-based wind power forecasting is the next step in optimizing the maintenance schedule. However, it is still in its early stages and presents many challenges. Accurately predicting the amount of power generated by wind turbines is particularly difficult due to their unpredictable behavior. AI-based forecasting systems must process multiple factors and large amounts of data quickly and accurately to provide timely predictions and optimize maintenance schedules.
In conclusion, the use of AI in wind power forecasting has had a significant impact on maintenance scheduling optimization. It has improved the accuracy of wind power forecasting, enhanced the reliability of wind energy, reduced maintenance costs, and increased the efficiency of wind farms.
As the demand for renewable energy grows, AI-based wind power forecasting and maintenance optimization solutions are becoming increasingly prevalent. These innovative startups use advanced machine learning algorithms and diverse data sources to predict and optimize wind energy production accurately. The market for AI in the energy sector is expected to experience tremendous growth in the coming years, with a predicted CAGR of 35.2%. With such a promising outlook, it is clear that the future of wind energy is closely tied to the advancements in AI technology.
We can also expect to see more tech collaborations and adoptions as companies seek to leverage the latest AI technologies to optimize the maintenance of wind turbines. Overall, the future looks bright for AI-based wind power forecasting and its impact on maintenance scheduling optimization.