Marmot is a data formatting and visualization tool for production cost and capacity expansion modelling results. It provides an efficient way to analysis data by combing temporally disaggregated results and allowing the aggregation of different device types and modelling regions.
Marmot currently supports analysis of PLEXOS production costs modelling and ReEDS capacity expansion results.
- Formats modelling results data to a standard format and saves them to a hdf5 file.
- Combines temporally disaggregated results.
- Handles every timestep whether that be 5 minute or yearly interval data.
- Provides approximately 120 pre-built plots which offer vast user customization through an easy to use configuration file and various mapping and input data csvs.
For detailed Installation Instruction see the docs at: https://nrel.github.io/Marmot/get-started/install.html
- To install the latest version
git clone --recurse-submodules https://github.com/NREL/Marmot.git
(Make sure to include --recurse-submodules else h5plexos will not be included correctly)
- Marmot includes a conda environment for linux users and requirements.txt file to ensure all dependencies are available. Users are advised to begin by trying the requirements.txt as current best practice.
The official documentation is hosted on github-pages: https://nrel.github.io/Marmot
Work on Marmot started at the National Renewable Energy Laboratory NREL (A national laboratory of the U.S. Department of Energy) in 2019 and
has been under active development since then.
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Some modifications to the structure of the PLEXOS model before running can facilitate analysis in Marmot, such as placing generators in meaningful categories for later mapping in Marmot and making sure any desired properties are enabled in the report.
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When running the Marmot formatter, it is often convenient to process all properties at once by setting all values in the plexos_properties.csv file to TRUE. Marmot will skip properties that are not available in a PLEXOS solution.
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View formatted contents: In order to check the contents of an existing processed HDF5 file, type the following in a Python terminal or workspace:
import pandas as pd temp=pd.HDFStore("path to formatted hdf5 file") temp.keys() temp.close()