Data Science When Applied to Plant Operations and Cost of Cycling

Learn how customers can quickly gain strategic advantage through the understanding of cycling costs and asset life prediction using Data Science.

Access our complimentary webinar where you will learn about basic benchmarking tools that allow power plants to compare their performance with their peers, providing critical information which can then be used to make smart data driven decisions by using big data analytics.
  

On Demand - English and Spanish Webinar Recordings

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Data Science is an interdisciplinary field that has been growing in every aspect of technology and is now being incorporated into the energy industry. The field of data science is divided into many disciplines such as analytics, machine learning, data visualization and big data.

Of particular interest is big data analytics. Big data analytics is a term which has widespread use within power plant monitoring and diagnostic groups and can mean different things to each individual. It is the process of examining large amounts of data to uncover hidden patterns and correlations. But how do we go about doing this?

Don't miss this opportunity to learn more about how Data Science can help power generators.  We will provide examples on how to manage large datasets, and will talk about how the latest tools in Data Science can be applied to Plant Operations and Cost of Cycling to give you Total Quality Assurance.
      
Who should attend this webinar: Power Utilities, Plant owners, Fleet Managers & Monitoring and Diagnostic Centers Operators.

Our Presenter

Martin Gascón is a data scientist with expertise in evaluating large data sets from fossil and renewable energy power plants. He then uses statistical analysis, mathematical modeling and machine learning techniques to generate predictive models to improve plant efficiency. Dr. Gascón earned a Ph.D. in Nuclear Physics developing detectors to study nuclear reactions. During a 2-year postdoc at Stanford and 3-year fellowship at Berkeley National Laboratory, he studied how to improve the functionality of radiation detectors by applying extreme conditions and using novel techniques and materials, with the objective of increasing the sensitivity of these detectors for national defense applications.
 
Dr. Gascón is the principal architect of Intertek's data analytics platform Ingrid. His current responsibilities include the cost of cycling, energy economic projects, statistical analysis and machine learning techniques for numerous energy projects for coal, gas and renewable energies. He has also developed programs and algorithms to predict the impact of renewable energy on fossil fuel plants.

 

Need help or have a question? +1 408 745 7000
 

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