November 10, 2021 - 11:00 AM - 12:30 PM Eastern Time (ET)
Applying Machine Learning
in Energy Policy Research
Machine learning, the use of algorithms and statistical models to analyze and draw inferences from patterns in high dimensional data, is seeing rapidly expanded application in the energy sector, for instance for improved forecasts of wind and solar energy availability, optimized control of complex energy systems, or predictive maintenance of energy systems. In research, machine learning provides new tools to analyze questions with relevance for energy policy decisions, such as the effectiveness of energy efficiency interventions or the probability and duration of weather-related electricity outages. In this webinar, Erica Myers, Associate Professor of Economics at the University of Calgary, and Christopher R. Knittel, George P. Shultz Professor and Faculty Director of MIT CEEPR, will share insights from their latest work using machine learning for energy policy research.