
Observability in Distribution Grids
Full grid transparency at the lowest cost
Preparation of Grid Models
We prepare the grid models so they can be used for following processes. The result is a computable grid model in CGMES format, the digital (grid) twin.
Utilising Measurement Data
The gridhound software analyses your measurement data and checks for errors and inconsistencies. Measurement data is an essential element for further digital processing, e. g. also for state estimation.
Optimal Measurement Placement
Based on information theoretical methods, our software determines the grid points with the maximum information density. This allows the minimization of required hardware and ensures an optimum selection of the measurment points, also taking into account economic aspects.
State Estimation
Our AI system is able to learn the behaviour of the distribution grid and to estimate the electrical parameters at all other network elements on the basis of very few measurement points. This enables us to establish complete observability in the distribution grid.
GRAICE
Our patented AI processes form the core of our product suite Graice
(GRid Artificial Intelligence Computing Engine).
Our product suite Graice is constantly being improved in cooperation with our customers.
WORKING FOR GRIDHOUND
Exciting tasks in an innovative team:
#Python
#Start-Up
#Energy Transition
Do you have solid programming experience, ideally with Python, and do you implement algorithms and data structures with confidence? Your heart beats for clean code and an agile approach?
#Smart Grid
#Distribution Grid
#Power Systems
You have successfully completed your education in Electrical Engineering with a focus on Electrical Power Engineering? You have a good understanding of grid calculation, grid simulation and grid components?
#Energy Transition
#Start-Up
#Python #Distribution Grids
We are looking for Student Assistants (f/m/d) as Software-Developers Back-End and as Electrical Engineer – Power Engineering.
GRIDHOUND
We create observability in the distribution grid!
gridhound is a spin-off of E.ON Energy Research Center at RWTH Aachen University and has positioned itself as the leading AI start-up in the field of state estimation for distribution grids.
The use of AI provides the energy industry with much needed data – especially for operation and planning. gridhound promotes the energy transition through its contribution to digitalisation, automation and transparency in the distribution grid.


Advisory Board
Our company advisory board is built up of high-class experts:

Robert Hienz is COO of the Mobility House scale-up and previously held various executive positions in German and European energy companies (including CEO E.ON Energie Deutschland GmbH, CFO E.ON France S.A.S.). With his in-depth knowledge of the European energy sector and his many years of management and sales experience, he supports gridhound in its business development and in further establishing itself in the European market.

Professor Antonello Monti is head of the Institute for Automation of Complex Power Systems at the E.ON Energy Research Center (ERC) of RWTH Aachen University and group leader at Fraunhofer FIT, Center for Digital Energy, Aachen. Before joining RWTH Aachen University, he was a Ph.D. Professor of Electrical Engineering at the University of South Carolina. gridhound is a spin-off of the E.ON ERC and Professor Monti continues to support the company as a visionary and idea provider.
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