Causal AI in the Energy Industry: Lessons learned at TotalEnergies

The Causal AI Conference 2022: Causal AI in the Energy Industry - Lessons learned at TotalEnergies Antoine Bertoncello Head of Next Generation AI, TotalEnergies One increasingly popular approach is to use machine learning algorithms to predict the future behaviour of a system (e.g., failure of equipment, the energy produced by renewable assets…) based on correlations found in the data. However, a prediction is sometimes insufficient, and one may want to know what will happen if a specific variable is changed to minimize the failure rate or optimize production. This step is particularly difficult because it requires going beyond correlations and instead inferring a causal relationship between variable A and outcome B. The concept of causality is, in general, answered through experiments, such as randomized control trials, experimental design, and simulation. However, in industrial settings, experiments are not feasible, and engineers and data scientists often only have access to observational data on the impacts on productivity. Epidemiologists, economists, and computer scientists have recently developed a range of statistical tools to go beyond correlation to discover and infer causal relationships. However, their use remains scarce in the energy world. In the last three years, TotalEnergies launched a research project to assess the use of causal inference in the company. In this talk, Antoine will describe a selection of the business cases that were identified, the approaches used, as well as the new ones we are developing with their academic partners. Learn more and join the community here: https://www.causalaiconference.com/ 00:00 - Welcome 00:50 - Causal AI in the Energy Industry: Lessons learned at TotalEnergies 23:50 - Q&A