Sigrid Passano Hellan: Hyperparameter tuning, Bayesian optimisation and applications related to climate change

If you don’t want to be setting and tuning machine learning model hyperparameters such as the learning rate yourself, then hyperparameter tuning is a good alternative. Also called hyperparameter optimisation, it has been developed to reduce the amount of machine learning experience required to set up a model by automatically finding the best hyperparameters. By reducing these hurdles to adoption, machine learning can be adopted for more climate applications. Bayesian optimisation is a popular method for hyperparameter optimisation, and consists of fitting a probabilistic model and using that to inform the optimisation process. Both hyperparameter optimisation and Bayesian optimisation can be greatly enhanced using transfer learning, where we learn from previous optimisation problems to speed up the current one.

In this talk I will give an introduction to methods for hyperparameter tuning and Bayesian optimisation, and how transfer learning can be used. Then I will present how Bayesian optimisation has been applied to climate related research, as we discussed in a recent survey. We found the main application areas to be material discovery – finding better materials for solar panels; wind farm layouts – deciding where to place turbines; optimal control of renewables – e.g. adjusting wind turbine blade angles to current conditions; and environmental monitoring – e.g. finding pollution maxima.

Sigrid Passano Hellan is a senior researcher at NORCE Research and the Bjerknes Centre for Climate Research in Bergen. She holds a PhD in Data Science from the University of Edinburgh, where she worked on Bayesian optimisation, transfer learning, hyperparameter optimisation and air pollution monitoring. Her interests combine machine learning methodology with environmental applications. Currently, she is working on downscaling weather data and forecasting icing on wind turbines.