Internship opportunity Bayesian Machine Learning on current...
Alliander aims to play a decisive role in the energy transition to a more sustainable future. The company seeks new technologies and responds to new developments in the energy sector with the goal to use the grid more efficiently and boost productivity. It allows us to connect as many renewables, electric vehicles, heat pumps and new customers to the grid as possible.
Alliander has measurements at various locations in its electricity network, to get insights in the currents, voltages and active/reactive powers in the grid (https://en.wikipedia.org/wiki/AC_power). Most of these measurements only measure the absolute value of the current (I), because these measurements are cheaper and more reliable then measurements of the active and reactive powers (P & Q). The latter, however, provide more insight in the behavior of the grid and different loads, and are much more suitable to do e.g. load forecasting and State-Estimation. Is it possible to estimate the direction of the power flow or even the fraction between P and Q (called the power factor: https://en.wikipedia.org/wiki/Power_factor) from absolute current measurements?
There is data from locations/substations where we do measure all the different quantities, which can be used to train a machine learning model, but there are also physical laws that constraint the solution space. For example there is Kirchhoff's current law that states that all incoming and outgoing (complex) currents or powers should add up to zero. This problem therefore lends itself very well to a Bayesian approach where physical knowledge and knowledge from data is combined.
Because the relations between different nodes in the network don’t need to be taken into account, this problem seems to be reasonable to solve within the period of one master thesis project.
You are a university student with a mathematical, artificial intelligence and/or computer science background. You are also interested in modeling complex systems and working with large amounts of data, and you are looking for a graduation or internship.
You also have:
Programming experience with regard to modeling and/or machine learning, for example in Python or R
Knowledge with Bayesian Statistics or even Bayesian Machine Learning
Interest in the energy system and the challenges of the energy transition
What do we offer you?
This is a challenging and highly varied internship in an organization that is at the center of the energy transition. Alliander is at the forefront of applying Data Science in a technical environment. Obviously, this includes a good internship allowance and we support you with all means to perform your work well. We have more than enough data available and ready to perform this assignment.
Alliander is a large Dutch distribution system operator (DSO) that ensures that millions of customers have access to electricity and gas every day for living, working, transport and recreation. We stand for an energy supply that gives everyone access to reliable, affordable and sustainable energy under the same conditions. Now and in the future. That is what we work on together every day. We offer our professionals an environment for innovative and smart ideas. An environment for your energy.
You will work at the Research Centre for Digital Technologies, This team is researching smart and innovative technologies that help us do our job better and faster in the field and in the office. We are researching which digital innovations will bring real value to Alliander and which should therefore be implemented on a large scale in (digitalization) teams. This means we're looking for radical digital technological innovations that will help Alliander make great progress. The rapid testing of theory in practice is part of our research, so that the most valuable digital technological innovations are ultimately implemented. Our Research Center innovates in an open manner and with an open mindset: connecting knowledge from outside and inside through collaboration and partnerships.
Alliander screens all applicants. Depending on the position, the screening consists of the following steps: checking references, checking the authenticity of identity papers and diplomas, an integrity check and requesting a certificate of conduct (VOG).