Load forecasting

Congestion Management


Within the current society we are used to and dependent on a reliable and affordably energy supply. Also, we see an increase of:

Preventing capacity problems in the electricity grid is one of the main challenges of Dutch Network operators. The business needs predictions of electricity demand to select potential congestion areas. In these areas flexible load is offered for managing congestions.

Therefore, I worked on:

Also monitoring training experiments and model performance is part of the project.

The end result is a deployed python package in production that generates predictions on a 15-min interval .
These predictions are used by decision makers to offer flexible load in potential congestion areas.


Project lead and stakeholder management.


Python, Pandas, Snowflake, scikit-learn, MLflow, Docker, Gitlab CI/CD.

Project portfolio

Selected Works

Load forecasting

Congestion Management

Forecasting electricity load of congestion points in the electricity grid by combining data from sensors and weather forecasts.

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Predictive maintenance

Preventing Excavation Damages

Predicting costly excavation damages and delivering the results automatically to an operational prevention team.

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Big Data solutions

Cloud computing

Developing a big data solution including a dashboard for analyzing high volumes of noisy sensory data.

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Talks and publications

Recently, I have been invited to speak about Data Science developments. I also published scientific papers about my work.

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