- 'ESS Data.xlsx' contains dataset for ESS installations. Obtained from https://www.sandia.gov/ess-ssl/global-energy-storage-database-home/ : Global Energy Storage Database Projects (11-17-2020). Accessed on Jan 18 2021.
- Data for elevation is obtained via Google Maps API. Refer to 'Get Elevation Data.py'
- Data for temperatures (contained in 'absolute.nc') is obtained from University of East Anglia Climatic Research Unit https://crudata.uea.ac.uk/cru/data/temperature/ : Absolute temperatures for the base period 1961-90 on a 5° by 5° grid (Jones et al., 1999). Refer to 'Temperature.py'
- Implements machine learning model to predict type of energy storage system based on features of
- Rated power in kW
- Discharge duration in hours
- Elevation of site
- Elevation difference (i.e. topography) of site
- Min, max and mean temperatures of site
- 'Model Training.ipynb' is the file used for model training
- 'Final Model for Prediction Use' is the file to use for prediction on unseen samples
- Packages
- numpy
- pandas
- matplotlib
- seaborn
- sklearn
- xgboost