An open-source bottom-up stochastic model for generating multi-energy load profiles (RAMP Website , RAMP Documentation)

What is RAMP

RAMP is an open-source software suite for the stochastic simulation of any user-driven energy demand time series based on few simple inputs.

The project aims to provide synthetic data wherever metered data does not exist, such as when designing systems in remote areas. Check out the documentation and learn more on the RAMP world from our website!

Recommended installation method

RAMP has been successfully installed and used on macOS, Windows and Linux.

The easiest way to make RAMP software working is to use the free conda package manager which can install the current and future RAMP dependencies in an easy and user friendly way.

To get conda, download and install "Anaconda Distribution", or "miniconda" which is lighter. You can install RAMP using pip, conda or from source code.

Installing through pip

1. To install the RAMP software, we suggest to create a new environment by running the following command in the Anaconda prompt:

conda create -n ramp python=3.10

2. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing the following line in the Anaconda Prompt:

conda activate ramp

1. Now you can use pip to install rampdemand on your environment as follow:

pip install rampdemand

Installing through the source code

You can also install RAMP from the source code! To do so, you first need to download the source code, which can be done in two ways:

• You can use git to clone the repository via:

git clone https://github.com/RAMP-project/RAMP.git

• Or, you may download the source code directly from:

"RAMP GitHub Repository".

In this second case, the source code will be downloaded as a zip file, so you'll need to extract the files.

After downloading the source code using any of abovementioned methods, you'll need to use your anaconda prompt to install it. There are two options again:

• You may follow the first two steps mentioned in Installing through pip. Then, change the directory in the prompt to the folder where the source code is saved (where you can find the setup.py file). To install the RAMP software, you may then use:

python setup.py install

• Alternatively, without taking any prior action, simply change the directory in the prompt to the folder where the source code is saved and then use:

conda env create -f environment.yml

Quick start

There are different ways to build a model using RAMP! Here, we provide a first example but you can find more information in our documentation.

Example python input files

Three different input files are provided as example representing three different categories of appliances that can be modelled with RAMP. To have a look to the python files, you can download them using the download_example function:

from ramp import download_example

download_example("the specfic folder directory to save the files")

• input_file_1.py: represents the most basic electric appliances; it is an example of how to model lightbulbs, radios, TVs, fridges, and other electric appliances. This input file is based on the ones used for the first RAMP publication.
• input_file_2.py: shows how to model user-driven thermal loads, with the example of a “shower” appliance. The peculiarity of thermal appliances is that the nominal power can be provided as external input as a “.csv” file (in this case, shower_P.csv). For the example “shower” appliance, the varying nominal power accounts for the effect of groundwater temperature variation throughout the year. This input file is based on that used for this publication.
• input_file_3.py: represents an example of how to model electric cooking appliances. In this input file two different kind of meals are modelled: 1) short and repetitive meals (e.g. breakfast); and 2) main meals (e.g. lunch, dinner). Repetitive meals do not vary across days, whilst main meals do so. In particular, every household can randomly choose between 3 different types of main meal every day. Such variability in meal preferences is modelled by means of two parameters: the user preference and the preference index. The user preference defines how many types of meal are available for each user to choose every day (e.g. 3). Then, each of the available meal options is modelled separately, with a different preference index attached. The stochastic process randomly varies the meal preference of each user every day, deciding whether they want a “type 1” meal, or a “type 2”, etc. on a given day. This input file is used in this publication

You can execute python input files within an IDE, in your terminal with python command

python