New technical disputes are now involving massive sets of data that need to be analysed and in many cases used to predict outcomes many years into the future. Traditionally, these data sets were analysed by experts taking long time while being limited by availability of applications and models capable of accurately extrapolating future trends.
Now, with the availability of AI and Machine Learning tools, experts are getting better equipped to thoroughly analyse massive data sets and build mathematical models that can predict many outcomes of complex process at a fraction of the time while providing greater accuracy.
This webinar explores how AI and machine learning tools are used to analyse data and predict outcomes of complex technical processes.
After explaining the new capabilities and how these can be applied to a large number of disputes, we will share real life experiences from cases we worked on where we built accurate models using machine learning to predict performance of renewable power plants through its life cycle.
We will also look at methods where AI was crucial in identifying anomalies and trends in SCADA data from a gas turbine and other critical systems.
Learning Objectives
By the end of the webinar, participants will:
Learn about AI techniques for expert analysis of large sets of data.
Explore how integrated AI-modelling frameworks are built, validated, and optimised for real-world applications.
Learn how and where AI tools and machine learning techniques can be deployed to assist in preparation of expert evidence in technical disputes.
Review case studies, including performance guarantee in a PV power plant dispute, and analysis of SCADA data from a gas turbine.
