Anomaly detection in the energy grid
Organizations consuming natural gas or electricity tend to show quite predictable behaviour. It should also be possible to detect sudden changes in energy usage. The goal of this research is to build a Machine Learning model capable of detecting these outliers.
- Artificial Intelligence
- Machine Learning
What do you get
- A challenging assignment within a practical environment
- € 1000 compensation, € 500 + lease car or € 600 + living space
- Professional guidance
- Courses aimed at your graduation period
- Support from our academic Research center at your disposal
- Two vacation days per month
What you will do
- 65% Research
- 10% Analyze, design, realize
- 25% Documentation
Organizations consuming natural gas or electricity tend to show quite predictable behaviour. For instance, the energy usage of offices is high on working hours and low otherwise, while the energy usage of a large factory with employees working in shifts will be quite constant. The amount of energy consumed can also vary based on seasons. A typical public outdoor swimming pool, for example, will use most energy in the summer months, since it is closed during the winter.
Since it is possible to estimate the energy consumption such companies relatively well, it should also be possible to detect sudden changes in energy usage. Such changes could have a legitimate cause, for example, an actual change the amount of energy required by a company, but could also be caused by e.g. metering errors, faulty machines at a factory, or changes made to the energy grid.
The goal of this research is to build a Machine Learning model capable of detecting these outliers. For instance, by applying clustering algorithms. The model should also try to determine whether the outlier was caused due to a metering error. The correctness and reliability of the model is from utmost importance.
We believe this assignment is quite challenging on its own. However, if you seek an even greater challenge, try to correct any metering error automatically, in addition to the other tasks mentioned. Again, by applying AI techniques, of course!
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