The contents of this repository are all in-progress and should not be expected to be free of errors or to perform any specific functions. Use only with care and caution.

The Res-IRF model is a tool for simulating energy consumption and energy efficiency improvements in the French residential building sector. It currently focuses on space heating as the main usage. The rationale for its development is to integrate a detailed description of the energy performance of the dwelling stock with a rich description of household behaviour. Res-IRF has been developed to improve the behavioural realism that is typically lacking in integrated models of energy demand.

Documentation is freely available on https://cired.github.io/Res-IRF/

A simple user interface is available on http://resirf.pythonanywhere.com/ to give an overview of Res-IRF main output.

Step 1: Git clone Res-IRF folder in your computer.

• Use your terminal and go to a location where you want to store the Res-IRF project.
• git clone https://github.com/CIRED/Res-IRF.git

Step 2: Create a conda environment from the environment.yml file:

• The environment.yml file is in the Res-IRF folder.
• Use the terminal and go to the Res-IRF folder stored on your computer.
• Type: conda env create -f res-irf-env.yml

Step 3: Activate the new environment.

• The first line of the yml file sets the new environment's name.
• Type: conda activate Res-IRF

Step 4: Launch Res-IRF

• Launch from Res-IRF root folder:
• python project/main.py -n project/input/phebus/config.json
• project/input/phebus/config.json is the path to the configuration file

Project includes libraries, scripts and notebooks.
/project is the folder containing scripts, notebooks, inputs and outputs.

The standard way to run Res-IRF:

Launch Res-IRF main script.
The model creates results in a folder in project/output.
Folder name is by default ddmmyyyy_hhmm (launching date and hour). By default, only a selection of the most important results are available and graphs.

To get a detailed view of the results add o True. Detailed results are .pkl files (serialize format by the pickle library).

A configuration file must be declared. An example of configuration file is in the input/phebus folder under the name of config.json. The Res-IRF script use Multiprocessing tool to launch multiple scenarios in the same time.

In the output/ddmmyyyy_hhmm folder:

• One folder for each scenario declared in the configuration file with detailed outputs:
  • detailed.csv detailed output readable directly with an Excel-like tool
  • summary_input.csv summary of main input
  • copy of parameters.json and config.son used for the run
• .png graphs comparing scenarios launch in the same config file.

Launch one of the Jupyter Notebook analysis tool (work in progress)
There are 4 main notebooks:

• ui_unique.ipynb: macro and micro output analysis.
• quick_comparison.ipyb: macro and micro output comparison.
• ui_comparison.ipyb: macro and micro output comparison.
• policy_indicator.ipyb: macro and micro output comparison and calculation of efficiency and effectiveness.

Notebook templates are stored in project/nb_template_analysis.
Users should copy and paste the template notebook in another folder to launch it.

The development of the Res-IRF model was initiated at CIRED in 2008. Coordinated by Louis-Gaëtan Giraudet, it involved over the years, in alphabetic order, Cyril Bourgeois, Frédéric Branger, François Chabrol, David Glotin, Céline Guivarch, Philippe Quirion, and Lucas Vivier.

If you find Res-IRF useful, please kindly cite our last paper:

@article{
  author  = {Giraudet, Louis-Gaëtan and Bourgeois, Cyril and Quirion, Philippe},
  title   = {Policies for low-carbon and affordable home heating: A French outlook},
  journal = {Energy Policy},
  year    = {2021},
  volume  = {151},
  url     = {https://www.sciencedirect.com/science/article/pii/S0301421521000094}
}

Lucas Vivier – @VivierLucas – [email protected]

Distributed under the GNU GENERAL PUBLIC LICENSE. See LICENSE for more information.

https://github.com/CIRED/Res-IRF