The main objective of this presentation is discussing applications of data centric methodologies for wind energy operation analysis and performance improvements. Due to uncertain nature of wind as a source of energy, predicting available electricity requires detailed and accurate performance modelling. Availability of accurate operational data is critical for monitoring overall electrical generation performance, equipment health, operational excellence and grid integration.
Challenges and opportunities for ongoing industry wide digitalization initiatives, as well as research into analytics techniques such as statistical and AI/ML modelling will be discussed. Importance of standardized data models and metadata as the core foundation of digitalization initiatives for consistent AI/ML modelling, will be also included in the presentation.
Alan Rezazadeh, has been working for about three decades in Alberta as a data analytics and governance specialist with focus on energy sector and academia research projects. His recent research engagements include oil and gas emission reduction, wind energy and electrical generation production optimization. Alan earned a PhD degree in Computer Science from the University of Regina, with a focus on applied mathematics and numerical analysis.
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