Harnessing the Power of Wind: Smart Energy Solutions through Advanced Forecasting
Summary: Wind energy is essential for renewable power generation, but integrating it into the electrical grid requires accurate wind forecasts. This study, part of the WindIO project, tackles the challenge of predicting local airflows. It shows that combining historical weather data with wind turbine measurements and simulations can improve weather and performance forecasts. Applying these methods can lead to better integration of renewable energy and more cost-effective wind power generation.
In the quest for sustainable energy solutions, wind power has emerged as a key player. However, integrating wind energy into the electrical grid relies on precise wind forecasts. The WindIO research project, led by the University of Bremen, explores the possible uses of ‘smartifying’ wind turbines. This initiative, in collaboration with partners like CONTACT Software GmbH, leverages IoT infrastructure and sensor data from research wind turbines to predict various operational states.
A key component of WindIO is a feasibility study conducted by CAIQ GmbH, which explores the enhancement of performance forecasts for existing wind turbines. This study marries stochastic methods with simulation tools like OpenFAST, aiming to refine wind and power forecasts. Such precision is crucial for grid operators and wind farm managers to seamlessly integrate wind energy, minimize penalties for deviations from planned outputs, and optimize maintenance activities.
One of the study’s focal points is the significant spatial uncertainty inherent in weather forecasts, especially concerning local airflows. By harnessing historical regional weather data and specific measurements from wind turbines, the research proposes regression models that localize weather forecasts, thereby improving performance predictions.
To collect localized weather data, a Krogmann 15/50 research wind turbine situated in Bremerhaven was equipped with sensors and telemetry devices. Over the course of a year, this setup amassed a comprehensive dataset, offering a wealth of information for in-depth analysis and the training of predictive models.
In pursuing the most accurate forecasting model, the study rigorously tested various regressors. After extensive evaluation, it was determined that AdaBoost, an ensemble learning method, yielded the most promising results. This technique, known for its ability to boost the performance of relatively simple models, stood out for its precision and reliability in predicting wind energy outputs, solidifying its position as the superior choice among the tested regressors.
The findings, showcased on a CONTACT Elements for IoT System, demonstrate the practical application and utility of these models.
The results from the spatially enhanced weather forecasts, powered by Machine Learning, proved to be promising. They offer a much more accurate performance forecast than raw regional weather predictions provided by services like OpenWeatherMap.
Moreover, the methodology described in the study opens the door to directly predicting energy yields by training models with historical performance data instead of weather data. This approach bypasses the physical modeling of wind turbines and can potentially be applied to entire wind farms without the need for elaborate weather sensors on each turbine.
The application of this technology promises significant economic benefits for both plant operators and grid operators, marking a leap forward in the efficient and economical generation of wind energy.
References:
[1] Alfredsson PH, Segalini A. 2017 Wind farms in complex terrains: an introduction. Phil. Trans. R. Soc. A 375: 20160096. https://doi.org/10.1098/rsta.2016.0096
[2] Elgendi M., AlMallahi M., Abdelkhalig A., Selim M., “A review of wind turbines in complex terrain”. International Journal of Thermofluids. Volume 17, February 2023. https://doi.org/10.1016/j.ijft.2023.100289
[3] https://www.next-kraftwerke.de/wissen/einspeisemanagement
[4] https://openweathermap.org/api
[5] https://github.com/OpenFAST
[6] https://www.dwd.de/DE/leistungen/windkarten/ deutschland_und_bundeslaender.html
Credits:
https://www.brementestturbine.science (WindIO)
https://www.contact-software.com
https://caiq.eu/
About CONTACT Research. CONTACT Research is a dynamic research group dedicated to collaborating with innovative minds from the fields of science and industry. Our primary mission is to develop cutting-edge solutions for the engineering and manufacturing challenges of the future. We undertake projects that encompass applied research, as well as technology and method innovation. An independent corporate unit within the CONTACT Software Group, we foster an environment where innovation thrives.
