Create and deploy an interactive Energy Time Series Forecaster

Thomas Craessaerts

Data Scientist, Elia Group

Thomas is a data scientist at Elia Group, focusing on developing forecasting solutions for the grid development and energy market teams in Belgium. He has a background in electrical engineering and intelligent mechanics.

Thomas Craessaerts
Thomas Craessaerts
Session description

In this workshop, we will show you how to build your own auto-Time Series forecaster with an interactive front-end dashboard. The focus will be on energy data, using open source data from Elia Group’s open data initiative.

By the end of the workshop, you will have developed your own auto-ml interactive time series forecaster. You can check it out here:

In particular, we will cover:

  • How time series problems differ from other ML problems

  • Open Source packages for training time series models

  • Building an interactive front-end app in Streamlit where users can upload a new time series and get the best prediction model

  • Deploying your app to the cloud


  • Intro on how time series forecasting differentiates from other ML tasks

  • Getting familiar with Elia OpenData API

  • Integration of a time series algorithm (using Prophet)

  • Development of the User Interface with Streamlit

  • Deployment on Streamlit Cloud

Expected knowledge:

  • Basic knowledge of Python, querying APIs, and Git

  • Familiar with pandas/data manipulation


  • Python (3.8.13)

  • IDE (like Visual Code Studio / PyCharm / etc.)

  • JupyterNotebooks

  • Anaconda

  • Git + GitHub account

For the workshop, we are going to use the libraries prophet and streamlit. Please create a virtual environment with the two packages installed before the workshop.