Source Code and Data for "Data-driven Characterization of Cooling Needs in a Portfolio of Co-located Commercial Buildings"

Published: 4 July 2024| Version 1 | DOI: 10.17632/ggxn7tgd42.1
Contributors:
Aqsa Naeem,
,

Description

This repository contains notebooks to characterize the cooling requirements in a portfolio of co-located commercial buildings by means of data-driven models and different visualizations including those of model estimates. The code can be used to reproduce the figures that are included in the publication "Data-driven Characterization of Cooling Needs in a Portfolio of Co-located Commercial Buildings", iScience.

Files

Steps to reproduce

1. Download and extract the code folder. It already contains the required dataset. 2. Required Python packages are provided in the requirements.txt file. Install any missing dependency. 3. Open terminal (command prompt) and navigate to the directory where the code has been downloaded. Run the following command to train the models. $ python make.py models Once the models are trained, generate the figures by using the following command. $ python make.py figs Note that both steps can be achieved in one step using the following command: $ python make.py all

Institutions

Stanford University

Categories

Commercial Energy Consumption, District Cooling System, Cooling Load, Energy Modeling

Licence