Building energy statistical modelling

Building Energy Geeks is where I write tutorials on everything related to my research.

The topic of this book is statistical modelling and inference applied to building energy performance assessment. It has two target audiences: building energy researchers and practitioners who need a gentle introduction to statistical modelling; statisticians who may be interested in applications to energy performance.

The first part of the book covers the motivation and theoretical background: an overview of the possibilities of data analysis applied to building energy performance assessment, and of the main categories and challenges of data analysis methods; a description of building physics and how they can be formulated as statistical models; the main steps of a Bayesian workflow for statistical modelling and inference, which aims at making sure that models are well defined and trained for a given application.

Then, the rest of the book shows some applications. It is a series of R and Python notebooks classified into chapters, each focusing on a type of model. The notebooks are self-sufficient, either based on R or Python, and mention whether non-standard libraries or other software should be installed.

  • Regression and mixture models
  • Time series analysis
  • State-space models
  • Gaussian Process models

Handbook of building energy efficiency

I teach heat transfer in buildings and HVAC systems at the Polytech engineering school, Université Savoie Mont-Blanc. I put up some online resources for students on these topics. ebeclim.org is a website where I post short videos about HVAC and heat transfer in buildings. It is originally meant as supplementary material for my students.

Here is one example about the prediction of air flow in buildings:

I am currently writing a more complete handbook on building energy efficiency, covering heat and mass transfer phenomena, HVAC systems and the design of passive buildings. This book will soon replace ebeclim.