MEC_51057_EP - Apprentissage Automatique pour le climat et l'énergie (2024-2025)

COURSE PHILOSOPHY

The amount of data produced in environmental sciences (energy and climate) paves the way to new applications. While traditionnal statistical methods are still essential, advanced machine-learning methods are more and more needed to make sense of big data, whether for analysis or to make predictions. One goal of machine learning is to extract identifiable patterns from these complex data sets. These patterns can then be used to take informed decisions. Another is to model relationships between different variables and then use these models to predict one variable from information of the other.

Examples of such big data sets are:

• If we want to optimize the energy performance of a building, we can set sensors in different places of the building that will give us a good overview of the energy consumption and energy loss. Analyzing such data set with machine learning will help us predict or optimize our energy consumption.
• The IPCC provides forecast of the average temperature for the next 100 years. This forecast is based on about 30 different predictions made by complex models. Machine learning provides a way to track reliable patterns in this complex data set.

The objective of this course is to provide an introduction to machine learning in order to help the students apply relevant methods to analyze specific data sets. Different families of unsupervised and supervised methods will be covered, while also providing a general approach to validate and test results. We encourage students to develop their critical thinking skills when facing a new dataset and applying a method in order to draw robust conclusions. We illustrate this course with examples from environmental sciences. These datasets correspond to concrete cases of analysis presented in the form of ipython notebooks.

COURSE LINK TO N (SDGs)

This course allows students to acquire knowledge prior to approaching the following SDGs and deals with an area related to these SDGs:

• SDG 7: Ensure access to affordable, reliable, sustainable and modern energy for all
• SDG 13: Take urgent action to combat climate change and its impacts

How does the course take these SDGs into account?

The aim of this course is to acquire the fundamentals of machine learning in order to apply these methods to problems in climate science and energy systems studies. These two topics are related since energy consumption and renewable energy production are directly affected by meteorological conditions and their long term climatic change. Climate and energy systems thus share common properties such as being complex, spatially extended and variable. Furthermore, their study involves in particular extracting information from big datasets. Thus, while not the main focus of this class, students learn methods that can prove useful in improving our understanding of the climate system and its interaction with societies and in helping manage energy systems with larger shares of variables renewable energies.

PREREQUISITES

• Elementary data analysis in Python with numpy, pandas and matplotlib,
• Linear algebra (linear systems, inverse, eigenvalues and eigenvectors),
• Elements of probabilities and statistics (probability distribution and density, random variable, conditional expectation, variance, covariance, sample estimates).

GRADING POLICY

• 25% participation (short in-class MCQs at beginning of class),
• 25% mid-course coding and analysis exercise and course questions,
• 50% final presentation including final notebook and involvment in project.

LANGUAGE

English