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Systems","sub_category":"Energy System Modeling Frameworks","monthly_downloads":210,"total_dependent_repos":0,"total_dependent_packages":0,"readme":".. image:: https://img.shields.io/gitter/room/RAMP-project/RAMP\n   :target: https://gitter.im/RAMP-project/community\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n    :target: https://github.com/psf/black\n\n.. image:: https://badge.fury.io/py/rampdemand.svg\n    :target: https://badge.fury.io/py/rampdemand\n\n.. image:: https://readthedocs.org/projects/rampdemand/badge/?version=latest\n    :target: https://rampdemand.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://coveralls.io/repos/github/RAMP-project/RAMP/badge.svg?branch=main\n   :target: https://coveralls.io/github/RAMP-project/RAMP?branch=main\n\n.. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/source/_static/RAMP_logo_basic.png?raw=true\n   :width: 300\n\n\n*An open-source bottom-up stochastic model for generating multi-energy load profiles* (`RAMP Website \u003chttps://rampdemand.org\u003e`_ , `RAMP Documentation \u003chttps://rampdemand.readthedocs.io/en/latest/?badge=latest\u003e`_)\n\n\nWhat is RAMP\n============\nRAMP is an open-source software suite for the stochastic simulation of any user-driven energy demand time series based on few simple inputs.\n\nThe project aims to provide synthetic data wherever metered data does not exist, such as when designing systems in remote areas. Check out the `documentation \u003chttps://rampdemand.readthedocs.io/en/latest/?badge=latest\u003e`_ and learn more on the RAMP world from our `website \u003chttps://rampdemand.org\u003e`_!\n\n.. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/figures/Example_output.jpg?raw=true\n   :width: 600\n\nRecommended installation method\n===============================\n\nRAMP has been successfully installed and used on macOS, Windows and Linux.\n\nThe easiest way to make RAMP software working is to use the free conda package manager which can install the current and future RAMP\ndependencies in an easy and user friendly way.\n\nTo get conda, `download and install \"Anaconda Distribution\" \u003chttps://www.anaconda.com/products/individual\u003e`_, or `\"miniconda\" \u003chttps://docs.conda.io/en/latest/miniconda.html\u003e`_ which is lighter.\nYou can install RAMP using pip, conda or from source code.\n\nInstalling through pip\n----------------------\n1. To install the RAMP software, we suggest to create a new environment by running the following command in the *Anaconda prompt*:\n\n.. code-block:: python\n\n   conda create -n ramp python=3.10\n\n\n2. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing\nthe following line in the *Anaconda Prompt*:\n\n.. code-block:: python\n\n   conda activate ramp\n\n3. Now you can use pip to install `rampdemand` on your environment as follow:\n\n.. code-block:: python\n\n  pip install rampdemand\n\n\nInstalling through the source code\n----------------------------------\nYou 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:\n\n* You can use git to clone the repository via:\n\n.. code-block:: bash\n\n   git clone https://github.com/RAMP-project/RAMP.git\n\n* Or, you may download the source code directly from:\n\n`\"RAMP GitHub Repository\" \u003chttps://github.com/RAMP-project/RAMP\u003e`_.\n\nIn this second case, the source code will be downloaded as a zip file, so you'll need to extract the files.\n\nAfter 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:\n\n* 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:\n\n.. code-block:: bash\n\n   python setup.py install\n\n* 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:\n\n.. code-block:: bash\n\n   conda env create -f environment.yml\n\nQuick start\n===========\nThere are different ways to build a model using RAMP! Here, we provide a first example but you can find more information in our `documentation  \u003chttps://rampdemand.readthedocs.io/en/latest/?badge=latest\u003e`_.\n\nExample python input files\n--------------------------\nThree different input files are provided as example representing three different categories of appliances that can be modelled with RAMP.\nTo have a look to the python files, you can download them using the `download_example` function:\n\n.. code-block:: python\n\n   from ramp import download_example\n\n   download_example(\"the specfic folder directory to save the files\")\n\n-  ``input_file_1.py``: represents the most basic electric appliances; it is\n   an example of how to model lightbulbs, radios, TVs, fridges, and\n   other electric appliances. This input file is based on the ones used\n   for `the first RAMP publication \u003chttps://doi.org/10.1016/j.energy.2019.04.097\u003e`__.\n\n-  ``input_file_2.py``: shows how to model user-driven thermal loads, with the\n   example of a “shower” appliance. The peculiarity of thermal appliances\n   is that the nominal power can be provided as external input as a\n   “.csv” file (in this case, ``shower_P.csv``). For the example “shower”\n   appliance, the varying nominal power accounts for the effect of\n   groundwater temperature variation throughout the year. This input\n   file is based on that used for `this\n   publication \u003chttps://doi.org/10.3390/app10217445\u003e`__.\n\n-  ``input_file_3.py``: represents an example of how to model electric\n   cooking appliances. In this input file two different kind of meals\n   are modelled: 1) short and repetitive meals (e.g. breakfast); and 2)\n   main meals (e.g. lunch, dinner). Repetitive meals do not vary across\n   days, whilst main meals do so. In particular, every household can\n   randomly choose between 3 different types of main meal every day.\n   Such variability in meal preferences is modelled by means of two\n   parameters: the ``user preference`` and the ``preference index``. The\n   ``user preference`` defines how many types of meal are available for\n   each user to choose every day (e.g. 3). Then, each of the available\n   meal options is modelled separately, with a different\n   ``preference index`` attached. The stochastic process randomly varies\n   the meal preference of each user every day, deciding whether they\n   want a “type 1” meal, or a “type 2”, etc. on a given day. This input\n   file is used in `this\n   publication \u003chttps://doi.org/10.1109/PTC.2019.8810571\u003e`__\n\nYou can execute python input files within an IDE, in your terminal with python command\n\n.. code-block:: bash\n\n   python \u003cpath to .py input file\u003e\n\nor in your terminal with the ``ramp`` command, see `Command line options \u003ccmd_option_\u003e`_ below for more information.\n\nSpreadsheet input files\n-----------------------\n\nIt is also possible to use spreadsheets as input files. To do so, you\nneed to run the ``ramp`` command with the option ``-i``:\n\n.. code-block:: bash\n\n   ramp -i \u003cpath to .xlsx input file\u003e\n\n\n.. note:: You can input several files, separated from each others by a single blank space you can also input python files\n\n.. _cmd_option:\n\nCommand line options\n--------------------\n\nIn the command line you can also run .py input files\nIf you already know how many daily profiles you want to simulate you can indicate it with the ``-n`` option:\n\n.. code-block:: bash\n\n   ramp -i \u003cpath to .xlsx or .py input file\u003e -n 10\n\nwill simulate 10 daily profiles. Note that if you do not provide this option you will being prompted for the\nnumber of daily profiles within the console.\n\n\nIf you want to save ramp results to a custom file, you can provide it with the option `-o`\n\n.. code-block:: bash\n\n   ramp -i \u003cpath to .xlsx input file\u003e -o \u003cpath where to save RAMP outputs\u003e\n\n.. note:: You can provide a number of output files, separated from each others by a single blank space, matching the number of input files.\n\nOther options are documented in the help of `ramp`, which you access with the ``-h`` option\n\n.. code-block:: bash\n\n   ramp -h\n\n\nIf you have existing python input files from RAMP version prior to 0.5, you can convert them to\nspreadsheets input files. Simply run\n\n.. code-block:: bash\n\n   ramp_convert -i \u003cpath to the .py input file you wish to convert\u003e\n\nIf you want to save a RAMP model you created with a .py file into a spreadsheet refer to\nthis `example \u003chttps://rampdemand.readthedocs.io/en/latest/examples/using_excel/using_excel.html#exporting-the-database\u003e`_\n\nFor other examples of command lines options, such as setting date ranges, please visit `the dedicated section  \u003chttps://rampdemand.readthedocs.io/en/latest/examples/year_simulation/year_simulation.html#setting-date-range\u003e`_ of the documentation.\n\nBuilding a model with a python script\n-------------------------------------\n\n.. code-block:: python\n\n   # importing functions\n   from ramp import UseCase, User\n\n   # Create a user category\n   household_1 = User(\n    user_name = \"Household type 1\", # an optional feature for the User class\n    num_users = 10, # Specifying the number of specific user category in the community\n   )\n\nYou can add appliances to a user category by:\n\n.. code-block:: python\n\n   # adding some appliances for the household\n   radio = household_1.add_appliance(\n    name = \"Small Radio\", # optional feature for the appliance class\n    number = 1, # how many radio each household type 1 has\n    power = 10, # RAMP does not take care of units of measure (e.g., Watts), you must be consistent\n    func_time = 120, # Total functioning time of appliance in minutes\n    num_windows = 2, # how many time-windows the appliance is used in\n   )\n\n\nThe use time frames can be specified using the 'window' method for each appliance of the user category:\n\n.. code-block:: python\n\n   # Specifying the functioning windows\n   radio.windows(\n    window_1 = [480,540], # from 8 AM to 9 AM\n    window_2 = [1320,1380], # from 10 PM to 11 PM\n   )\n\nYou can also add another, different user to the simulation. In this case,\nwe use a more compact formulation:\n\n.. code-block:: python\n\n   # Create a second user category\n   household_2 = User(\n    user_name = \"Household type 2\", # an optional feature for the User class\n    num_users = 13, # Specifying the number of specific user category in the community\n    )\n\n   # adding some appliances for the new household type in compact form, with windows specified directly and random variability\n   light_bulbs = household_2.add_appliance(\n    name = \"Light bulbs\", # optional feature for the appliance class\n    number = 5, # how many light bulbs each household type 2 has\n    power = 7, # RAMP does not take care of units of measure (e.g., Watts), you must be consistent\n    func_time = 120, # total functioning time of appliance in minutes\n    time_fraction_random_variability=0.2, # 20% random variability associated to the total functioning time\n    num_windows = 2, # how many time-windows the appliance is used in\n    window_1 = [390,480], # from 6.30 AM to 8 AM\n    window_2 = [1020,1440], # from 5 PM to 12 PM\n    random_var_w=0.35 # 35% randomness assigned to the size of the functioning windows\n    )\n\nAt this point, we can group our different users into a \"use case\" and run the simulation,\nfor instance for a whole year.\n\n.. code-block:: python\n\n   use_case = UseCase(users=[household_1,household_2], date_start=\"2020-01-01\", date_end=\"2020-12-31\")\n   whole_year_profile = use_case.generate_daily_load_profiles()\n\nHere is your first load for a community including two types of housholds,\nfor a total of 23 individual users. Of course, more variations and many more\nfeatures are possible! For instance, you can simulate loads even for\nan individual appliance or user. In addition, you can use in-built plotting\nfunctionalities to explore your results. Check out the documentation\nfor all the possibilities.\n\nContributing\n============\nThis project is open-source. Interested users are therefore invited to test, comment or contribute to the tool. Submitting issues is the best way to get in touch with the development team, which will address your comment, question, or development request in the best possible way. We are also looking for contributors to the main code, willing to contribute to its capabilities, computational-efficiency, formulation, etc.\n\nTo contribute changes please consult our `Contribution guidelines \u003chttps://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md\u003e`_\n\n\nHow to cite\n===========\nPlease cite the original Journal publication if you use RAMP in your research:\n\n*F. Lombardi, S. Balderrama, S. Quoilin, E. Colombo, Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model, Energy, 2019,*\n`https://doi.org/10.1016/j.energy.2019.04.097 \u003chttps://doi.org/10.1016/j.energy.2019.04.097\u003e`_\n\nMore information\n================\nWant to know more about the possible applications of RAMP, the studies that relied on it and much more? 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