This Jupyter Notebook accompanies the article titled "Introduction to Energy Modelling - A ML Approach" on LSEG Developer Portal.
This article will introduce a simple approach to begin to model energy markets. In our case we will be looking to see if we can ascertain any impact on German energy prices when the wind blows. Energy markets are quite complex to model nevertheless we will try to have a go at this using some of the tools available in the Python scientific computing ecosystem. I hope to demonstrate that it is possible with a little effort.
Pre-requisites:
LSEG Workspace with access to LSEG Data Library for Python
Required Python Packages: lseg-data, pandas, numpy, matplotlib, geopandas, shapely,scipy, xgboost, scikit-learn