The article also presents case examples of how AI and ML are used in the wind energy sector. For instance, machine learning-based technology can detect yaw misalignment with 96 percent accuracy, and AI technologies can remotely detect pitch-bearing issues, predicting failures with over 90 percent accuracy up to six months in advance. The integration of AI with SaaS platforms has also proven transformative for the renewable energy sector, unlocking the power of big data and reducing the energy cost from wind.
Key takeaways:
- Artificial Intelligence (AI) and Machine Learning (ML) technologies can significantly improve the efficiency and reliability of wind energy operations by predicting maintenance needs and preventing equipment failure.
- Machine learning-based technology can identify yaw misalignment in wind turbines with 96 percent accuracy, improving energy capture and reducing stress on turbine components.
- AI technologies can remotely detect pitch-bearing issues in wind turbines, predicting failures with over 90 percent accuracy up to six months in advance, saving significant time and money.
- Integration of AI with Software as a Service (SaaS) platforms, particularly through the use of generative AI, can streamline renewable energy management, unlocking the power of big data and reducing energy costs.