
Attendees will gain insights into data collection, storage, and cleaning techniques, as well as advanced analytics methods applied to enhance reliability, efficiency, and sustainability in power networks. Real-world case studies and future trends, including AI-driven solutions and digital twins, will also be discussed, providing a practical understanding of how data-driven technologies are shaping the future of power engineering.
Key Takeaways:
🔹Understand the role of Big Data and Machine Learning in enhancing the efficiency, reliability, and sustainability of modern power systems.
🔹Learn about data lifecycle management in power networks, including best practices for data collection, cleaning, storage, and real-time analytics.
🔹Explore real-world applications and case studies showcasing predictive maintenance, demand forecasting, smart grid optimization, and the future of AI in power system management.
Presenter
Saeideh Sekhavat, EIT Lecturer
Saeideh Sekhavat (Sadie) is a Lecturer, Researcher, and IT professional with a strong background in mathematics and a focus on applying artificial intelligence in healthcare. She holds a bachelor’s degree in Applied Mathematics and a graduate degree in Systems Analysis from the National University of Singapore. Her experience includes the use of AI tools and deep learning techniques for medical image processing and analysis to support healthcare applications. Sadie has also been involved in projects related to data analysis, mathematical modeling, and computational image analysis. She is currently working as a full-time lecturer at the Engineering Institute of Technology (EIT), where she is passionate about delivering practical, industry-focused education.
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All content in this video is current as of the date of upload.
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